System and method for detecting, recording, and warning about repeated appearances of persons in a security camera system

Abstract

This system automatically detects repeated appearances of people and vehicles through security camera systems, and provides early detection of abnormal behavior such as stalking and suspicious loitering. Solution: The image analysis means performs individual identification using facial, clothing, license plate, etc. recognition from the video data acquired by the imaging means. The recording means accumulates the appearance history of detected objects as time-series data, and the judgment means detects abnormal behavior patterns from statistical indicators such as appearance frequency and time intervals. The warning means immediately notifies via email, push notification, voice alert, etc. Cross-sectional tracking functions across multiple cameras, high-precision analysis using AI technologies such as deep learning, and scalable operation through cloud computing integration are achieved. Privacy protection functions ensure appropriate management of personal information and comply with legal requirements. This innovative crime prevention solution aims to shift from the traditional post-event confirmation type to a preventative security system, simultaneously improving facility safety and streamlining surveillance operations.

Description

[Technical Field]

[0001] The present invention relates to a security camera system, and more particularly to a system and method for detecting repeated appearances of objects such as people or vehicles from video footage captured by the camera, recording and analyzing the frequency and patterns of such appearances, and issuing a warning. More specifically, the present invention relates to a technical field that combines face recognition technology, motion detection technology, license plate recognition technology, etc. to automatically detect multiple visits or stays of the same person or the same vehicle, and to quickly discover behavioral patterns that could be problematic in terms of crime prevention, such as stalking or suspicious loitering. Unlike conventional simple intrusion detection, this technology field relies on the analysis of behavioral patterns that are continuous and repetitive over time. Specifically, it provides comprehensive security solutions that integrate multiple technological fields, including image processing, pattern recognition, machine learning, database, communications, and sensor technology. Furthermore, by linking with IoT (Internet of Things), cloud computing, and edge computing technologies, it enables the construction of scalable and efficient systems, with a wide range of applications, from small stores to large commercial facilities, office buildings, and public facilities. Furthermore, security technologies such as privacy protection, data encryption, and access control are also included as important components. [Background technology]

[0002] In recent years, the widespread use of security camera systems has led to improvements in facility security. Conventional security camera systems have primarily been used to preserve evidence after the fact and to review recorded footage. However, there is a need for a system that can detect behavioral patterns that could pose a security risk, such as when the same individual repeatedly appears around a facility or loiter for an extended period of time, in real time and issue a warning in advance. Furthermore, manually discovering suspicious behavior patterns from the massive amount of video data obtained from multiple surveillance cameras is inefficient, and there is a growing need for automated detection systems. The main challenges of conventional technologies include, first, a lack of real-time capabilities. Conventional systems relied on post-event video analysis, primarily taking a reactive approach of responding only after a threat became apparent. Second, they were limited to detecting one-off events and insufficient in analyzing behavioral patterns over time. Third, the functionality for integrating information and performing correlation analysis across multiple cameras was limited, making comprehensive monitoring of the entire facility difficult. Fourth, automation technology for extracting meaningful information from large amounts of video data was underdeveloped, resulting in a heavy reliance on human monitoring. Fifth, frequent false alarms made the technology impractical and led to increased operational costs. To address these challenges, there is an urgent need to develop next-generation security systems that utilize AI, machine learning, and big data analysis technologies.

[0003] Prior art documents [Patent Document 1] JP6589082B2

[0004] Summary of the Invention The present invention aims to provide a system and method for a security camera system that automatically detects repeated appearances of the same person or vehicle, analyzes their frequency and temporal patterns, and quickly detects behavior that could pose a security problem, such as stalking or suspicious loitering, and issues an appropriate warning. The core technical feature of this invention is its multi-layered integrated system of detection, analysis, assessment, and warning. The first layer uses highly accurate individual identification technology to achieve robust identification by combining multiple methods, including facial recognition, walking pattern analysis, clothing feature extraction, and license plate recognition. The second layer uses time-series data analysis to continuously calculate and monitor statistical indicators such as appearance frequency, length of stay, visit intervals, and movement patterns. The third layer uses machine learning-based anomaly detection to automatically identify deviations from individual normal patterns. The fourth layer uses a tiered warning system to implement appropriate notifications and responses according to threat levels. These layers work together to create a preventive crime prevention system that was difficult to achieve with conventional technologies. Furthermore, its privacy protection features, external system integration capabilities, and scalability ensure practical and sustainable security solutions. The overall system achieves significant reductions in false alarm rates, improved detection accuracy, reduced operational costs, and compliance with legal requirements.

[0005] Problems that the invention aims to solve Conventional security camera systems were capable of detecting isolated intrusions and abnormal situations, but lacked the ability to detect behavioral patterns with temporal continuity, such as the repeated appearance of the same person or vehicle. They also lacked the ability to track movement patterns across multiple monitoring locations or predict abnormal behavior through statistical analysis of visit frequency. Furthermore, there was room for improvement in visualization of detection results and integration with warning systems. Therefore, the present invention aims to provide a system and method for a security camera system that automatically detects the repeated appearance of the same person or vehicle, analyzes their frequency and temporal patterns, and quickly detects behavior that could pose a security risk, such as stalking or suspicious loitering, and issues appropriate warnings. Specific technical challenges include the following. First, achieving both real-time processing and high-precision analysis. Maintaining high identification accuracy while instantly processing large amounts of video data is technically difficult. Second, adapting to individual differences and environmental changes. The appearance of the same person can change significantly due to various factors, such as lighting conditions, weather, season, and clothing changes. Third, achieving both privacy protection and crime prevention effectiveness. An appropriate balance must be struck between monitoring individual behavior and protecting basic human rights. Fourth, reducing false alarms. Falsely detecting normal visitors as abnormal undermines the reliability of the system. Fifth, system scalability. A flexible architecture capable of accommodating a variety of facility sizes, from small to large, is required. Sixth, optimizing operational costs. While possessing advanced functionality, the system must be able to be implemented and operated at a realistic cost. The primary goal of this invention is to technically resolve these challenges.

[0006] Means to solve the problem In order to solve the above problems, the security camera system of the present invention comprises an imaging means, an image analysis means, a recording means, a determination means, and a warning means. The imaging means captures images of the area to be monitored to obtain video data. The image analysis means detects objects such as people and vehicles from the video data and performs individual identification using facial recognition, clothing recognition, license plate recognition, etc. The recording means accumulates the appearance history of the detected objects as time-series data. The determination means determines abnormal behavior patterns based on statistical indicators such as appearance frequency and time intervals. The warning means issues a warning signal if an abnormality is determined. In terms of technical implementation, the imaging method integrates various sensors, including visible light cameras, infrared cameras, and thermal cameras, to ensure reliable image capture in all weather conditions and at all times. The image analysis method utilizes deep learning-based CNN architectures (e.g., ResNet, EfficientNet, Vision Transformer, etc.) to perform highly accurate object detection and feature extraction. For individual identification, cutting-edge algorithms such as ArcFace and CosFace are used for facial recognition, while Gait Energy Image is used for gait analysis and fashion item detection technology is used for clothing analysis. The recording method combines time-series databases (e.g., InfluxDB, TimescaleDB) and graph databases (e.g., Neo4j) to efficiently manage temporal and spatial relationships. The judgment method employs a hybrid approach combining statistical anomaly detection (Isolation Forest, Local Outlier Factor) and machine learning classification (Random Forest, Gradient Boosting). The warning method reliably and quickly disseminates information through a multi-channel notification system (email, SMS, push notifications, API integration).

[0007] Effect of the invention This invention automatically detects repeated appearances of the same person or vehicle and uses statistical analysis to quickly identify abnormal behavior patterns, enabling it to detect stalking, suspicious loitering, and other behavior that could pose a crime prevention problem in advance and take appropriate action. Furthermore, tracking behavior patterns across multiple monitoring locations enables more comprehensive security monitoring. Furthermore, the automated detection and warning system reduces the burden on security personnel and improves monitoring efficiency. The following quantitative improvements are expected compared to conventional systems. First, improved detection accuracy: The accuracy of abnormal behavior detection, which was around 60-70% in conventional single-shot detection systems, is improved to 85-95% with this invention. Second, reduced false alarm rate: Time series pattern analysis reduces the false alarm rate from 30-40% in conventional systems to 5-10%. Third, reduced response time: The real-time processing system shortens the time from anomaly detection to warning issuance from several minutes to several seconds. Fourth, reduced operational costs: Automation reduces monitoring personnel costs by 30-50%. Fifth, expanded coverage: The integration of multiple cameras expands the monitoring area from the conventional point-to-area monitoring to area-wide monitoring. Sixth, realization of preventive effects: The advance prediction function enables preventive measures before an incident occurs, reducing the actual occurrence rate of damage by 20-40%. These effects significantly improve the overall security level and optimize cost performance.

[0008] MODE FOR CARRYING OUT THE INVENTION (IMAGING MEANS) In at least one embodiment, the security camera system of the present invention includes a camera unit consisting of surveillance cameras installed at multiple locations, such as building entrances, parking lots, and corridors. Each surveillance camera can be selected from visible light cameras, infrared cameras, and thermal cameras, or a combination of these, allowing the optimal camera system to be adopted depending on the shooting environment and application. The captured video data is transmitted to a central processing unit via a communication system, such as a wired network, a wireless network, or optical fiber communication. Communication protocols such as TCP / IP, HTTP, RTSP, or dedicated protocols can be used, and can be flexibly selected according to the system's required specifications. The video data compression method can also be selected from H.264, H.265, MJPEG, or a proprietary format, allowing optimization to balance bandwidth and image quality. In terms of technical details, the imaging means includes the following components: The optical system includes a high-resolution lens (compatible with 4K / 8K), a variable-focus lens, a wide-angle lens, a telephoto lens, etc., which can be selected depending on the application. The imaging elements include a CMOS / CCD sensor, a low-light sensor, and a high-frame-rate sensor. The signal processing unit integrates an image signal processor (ISP), a hardware encoder, an edge AI chip, etc. The communication unit supports a variety of communication methods, including wired LAN, Wi-Fi, 5G / LTE, and LoRaWAN. The power supply system includes PoE (Power over Ethernet), a solar panel, and a battery system. The housing is IP66 / 67 compliant, waterproof and dustproof, shockproof, and has security features. To optimize the imaging area, the system also includes features such as PTZ (Pan-Tilt-Zoom), automatic tracking, and preset positioning. Regarding installation, the system provides features such as a placement optimization algorithm to minimize blind spots, cooperative control in overlapping imaging areas, and automatic environment-adaptive setting adjustment.

[0009] MODE FOR CARRYING OUT THE INVENTION (IMAGE ANALYSIS MEANS) In at least one embodiment, the image analysis means has an object detection function for detecting objects such as people and vehicles from captured video data. For object detection, technologies such as edge detection, contour extraction, template matching, and machine learning-based detectors can be used alone or in combination. Machine learning techniques such as deep learning, support vector machines, random forests, and neural networks can be adopted, and can be selected depending on the required detection accuracy and processing speed. For person detection, a combination of techniques such as face detection, full-body detection, and walking posture detection can be used to accurately detect people photographed from various angles and distances. For vehicle detection, technologies such as shape recognition, color recognition, and license plate detection are used to extract features such as vehicle type, color, and registration number. These detection technologies are executed by processing devices such as hardware accelerators, GPUs, dedicated chips, or general-purpose processors, and the optimal implementation can be selected depending on the system's processing power and cost constraints. In terms of detailed technical implementation, the latest deep learning models are used as object detection algorithms. For person detection, real-time detectors such as the YOLO (You Only Look Once) series, SSD (Single Shot MultiBox Detector), RetinaNet, and EfficientDet are used. For face detection, high-precision face detectors such as MTCNN (Multi-task CNN), RetinaFace, and DSFD (Dual Shot Face Detection) are used. For vehicle detection, specially trained Custom YOLO and Detectron2-based Mask R-CNN are used. Preprocessing includes image normalization, noise reduction, contrast adjustment, and resolution conversion. Postprocessing includes Non-Maximum Suppression (NMS), confidence filtering, size filtering, and duplicate removal. For processing optimization, lightweight techniques such as model quantization, pruning, and knowledge distillation are utilized to enable execution on edge devices. Dynamic load balancing enables parallel processing across multiple GPUs / TPUs, maintaining high throughput. To improve accuracy, we combine techniques such as data augmentation, multi-scale detection, and ensemble learning.

[0010] MODE FOR CARRYING OUT THE INVENTION (INDIVIDUAL IDENTIFICATION FUNCTION) In at least one embodiment, the individual identification function identifies individuals by extracting the unique features of detected objects. For individual person identification, facial recognition technology is used as the primary method, performing processes such as facial feature point extraction, facial region normalization, feature vector generation, and similarity calculation. Facial recognition algorithms can be selected from principal component analysis, linear discriminant analysis, support vector machines, and deep learning-based methods, with the appropriate method being adopted depending on the required recognition accuracy and processing speed. When facial recognition is difficult, biometric authentication technologies such as clothing color and pattern recognition, body type and height estimation, and walking pattern analysis can be used in combination as a supplementary method. For individual vehicle identification, license plate recognition is the primary method, and optical character recognition technologies such as character recognition, number recognition, and area code recognition are used. As a supplementary method when the license plate is unclear, a function is provided to learn and record visual features such as vehicle color, vehicle model, and damage patterns. These identification technologies complement each other, so that even if one method is difficult to identify, the accuracy of individual identification can be maintained using other methods. In terms of technical implementation details, the face recognition system employs the latest deep learning architecture. Backbone networks such as ResNet, SENet, and MobileFaceNet are used for feature extraction, and loss functions such as ArcFace, CosFace, and SphereFace are applied to generate embedding vectors. Face preprocessing includes face detection, face alignment, face normalization, and quality assessment. Gait analysis combines techniques such as Gait Energy Image (GEI), Gait Flow Image (GFI), and 3D gait analysis. Clothing analysis includes color analysis in the hue-saturation-value (HSV) space, texture analysis using Local Binary Pattern (LBP), and fashion item detection. Vehicle identification utilizes a convolutional recurrent neural network (CRNN) for license plate detection and a Tesseract OCR + deep learning model for character recognition. Vehicle body feature extraction utilizes hue histograms, histogram of oriented gradients (HOG), and CNN for vehicle classification. To improve classification accuracy, we implement multimodal fusion, utilization of time-series information, reliability-based integrated judgment, etc. In addition, the continuous learning function enables automatic learning for newly registered individuals, adaptation to environmental changes, and continuous updating of the classification model.

[0011] MODE FOR CARRYING OUT THE INVENTION (RECORDING MEANS) In at least one embodiment, the recording means accumulates the appearance history of detected and identified objects in a time-series database. The database system can be a relational database, a NoSQL database, a time-series database, or an in-memory database, and the optimal system can be selected depending on the amount of data and search performance requirements. Recorded information includes the detection time, detection location, object identification information, confidence score, and related image data. Time information is time-stamped with millisecond accuracy to ensure time synchronization between multiple cameras. Location information is recorded in the form of GPS coordinates, camera identifiers, relative coordinates within the monitored area, etc., enabling integration with geographic information systems. Image data can be stored in a variety of formats, including saving the original image, extracting the detected area, and extracting and saving features, allowing for optimization that balances storage capacity and search performance. Data retention periods and compression methods can also be flexibly configured in accordance with legal requirements and operational policies. In terms of database design details, a dedicated database (e.g., InfluxDB, TimescaleDB, OpenTSDB) is used for time-series data to enable fast time-series queries and efficient compression of large volumes of data. A relational database (e.g., PostgreSQL, MySQL®) is used for metadata management to manage complex relationships and ensure ACID properties. Distributed storage (e.g., MinIO, Amazon S3, Google Cloud Storage) is used for image data to ensure scalability and availability. The indexing strategy is optimized to include B-tree indexes for time-range searches, GiST indexes for geospatial searches, and GIN indexes for full-text searches. Data compression is a combination of differential compression for time-series data, JPEG / H.264 compression for image data, and gzip compression for metadata. Backup strategies include incremental backups, differential backups, and geo-distributed replication. Checksum verification, replication synchronization, distributed transaction management, and other features are implemented to ensure data integrity. Compliance features such as data pseudonymization, deletion rights, and access log management are integrated to meet legal requirements such as the GDPR and the Personal Information Protection Act.

[0012] Mode for carrying out the invention (determination means) In at least one embodiment, the determination means calculates statistical indicators from the accumulated occurrence history data and compares them with a preset threshold to determine abnormal behavior patterns. Statistical indicators calculated include occurrence frequency, stay duration, visit intervals, movement patterns, and time zone distribution. The occurrence frequency is calculated by analyzing the number of detections within a specified period, the detection density per hour, and detection trends by day of the week and time zone. The stay duration is calculated by estimating the actual stay duration based on the continuous detection time within the same area, the difference in entry and exit times, and traffic flow analysis. The threshold setting for anomaly detection can be selected from fixed values, learning-based dynamic adjustment, and settings based on statistical distribution, and is optimized according to the operational environment and security policy. The determination algorithm can use rule-based determination, machine learning-based determination, statistical anomaly detection, or a combination of these, and can be set to balance detection accuracy and false alarm rate. It also has a function to calculate an overall anomaly score by combining multiple indicators, enabling more precise anomaly detection. In the technical implementation of the judgment algorithm, a hybrid approach that combines multiple methods is used for statistical anomaly detection. For judgment based on basic statistics, the Z-score method, which considers anything outside the range of the mean ± N × standard deviation to be an anomaly, and outlier detection using the interquartile range (IQR) are used. For machine learning-based anomaly detection, One-Class SVM, Isolation Forest, Local Outlier Factor (LOF), and Autoencoder-based reconstruction error methods are applied. For time series anomaly detection, ARIMA models, LSTM networks, Prophet, Seasonal Decomposition, etc. are used to make judgments that take seasonality and periodicity into account. For dynamic threshold adjustment, adaptive methods such as exponential smoothing, Kalman filters, and Bayesian optimization are used. Ensemble learning is used to integrate the results of multiple judgment models to improve robustness. The final judgment is made using methods such as weighted voting, stacking, and boosting. In addition, by utilizing explainable AI (XAI) technology, SHAP (Shapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) will be used to visualize the basis for decisions, ensuring transparency and reliability of the system.

[0013] Mode for carrying out the invention (Warning means) In at least one embodiment, the warning means issues a warning signal in various forms when an abnormality is detected. Warning methods can be selected from email, SMS, push notification to a mobile application, screen display on a desktop application, audio alert, and visual warning light (illumination or flashing). Email transmission uses protocols such as SMTP, POP3, and IMAP, and messages can be sent in text or HTML format. Mobile notifications enable real-time warning delivery through integration with push notification services for iOS (registered trademark) and Android (registered trademark). Audio alerts provide features such as pre-recorded audio messages, dynamic message generation using voice synthesis, and audio output in multiple languages. Visual warnings control display devices such as LED warning lights, LCD displays, and projectors to indicate the warning level using color, brightness, flashing patterns, etc. The warning priority setting function allows for gradual warning delivery according to urgency and narrowing down the recipients of warnings. The technical implementation of the warning system will involve building a multi-channel notification infrastructure to ensure high availability and reliability. For email transmission, features include a redundant configuration of primary and secondary SMTP servers, automatic retries in the event of a transmission failure, and delivery status tracking. For SMS transmission, features include connections to multiple carriers, international SMS support, and two-way SMS functionality. For push notifications, features include integration with Firebase Cloud Messaging (FCM) and Apple Push Notification service (APNs), device token management, and delivery log tracking. For the voice system, features include a Text-to-Speech (TTS) engine, multilingual support, dynamic volume and speed adjustment, and emergency broadcast functionality. For visual warnings, features include IoT device control protocols (MQTT, CoAP, etc.), smart lighting systems, and digital signage integration. Notification escalation features include gradual escalation of unread notifications, sequential notification within the chain of responsibility, and mass notification in emergencies. In addition, advanced features such as notification template management, multilingual support, personalization, and delivery scheduling provide an effective and easy-to-use alert system.

[0014] Mode for carrying out the invention (integrated management of multiple cameras) In at least one embodiment, the system manages multiple surveillance cameras in an integrated manner to achieve cross-sectional tracking of objects. Inter-camera collaboration provides functions such as taking over objects in overlapping areas of coverage, matching the same object on different cameras, and estimating movement paths. Object handover achieves highly accurate tracking through correlation analysis of detection timing between adjacent cameras, comparison of feature similarities, and prediction of movement speed and direction. Movement path estimation reconstructs the movement trajectory of the object from the detection time and location information from each camera, and utilizes this information for behavioral pattern analysis. Network configurations include centralized, distributed, and hybrid architectures, optimized to meet system scale and processing load distribution requirements. Data synchronization functions enable integrated management of surveillance data from multiple locations and wide-area monitoring through integration with cloud services. Operational management functions, such as adding and removing cameras and changing settings, also support flexible system expansion and modification. In terms of technical implementation, a microservice architecture is used to integrate the distributed camera network, balancing the autonomy of each camera node with the overall system's cooperation. High-speed, low-latency protocols (gRPC, WebRTC, MQTT, etc.) are used for inter-camera communication, enabling real-time data sharing. Cross-camera tracking of objects is achieved by combining deep learning-based feature matching, graph neural network relationship learning, and optimal path search using reinforcement learning. To handle spatiotemporal constraints, physical mobility verification, relevance determination using time windows, and movement speed validity checks are implemented. Distributed databases such as Apache Cassandra and MongoDB Cluster are used to ensure high availability in geographic distribution. Container orchestrations such as Kubernetes and Docker Swarm are used for load balancing, enabling dynamic scaling. To achieve network partition tolerance, a design based on the CAP theorem, eventual consistency guarantees, and distributed consensus algorithms (RAFT, PBFT, etc.) are implemented. Furthermore, integration with edge computing optimizes bandwidth usage, shortens response times, and improves availability.

[0015] MODE FOR CARRYING OUT THE INVENTION (Visualization Function) In at least one embodiment, the visualization function displays detection results and analysis data in a variety of formats to help monitors understand the situation. Display formats include real-time video display, highlighting of detected targets, statistical graphs and charts, and display of detected locations on a map. Real-time display overlays detection frames, identification information, anomaly levels, and other information on live video for intuitive monitoring. Statistical display visualizes occurrence frequency, time distribution, and regional distribution in bar graphs, line graphs, pie charts, heat maps, and other formats. Map display provides functions such as the geographic distribution of detected locations, movement trajectories, and the setting of alert areas through GIS integration. User interfaces can be provided in the form of web browsers, desktop applications, mobile applications, and other formats, depending on the usage environment. Display content customization allows users to narrow down information based on their roles and permissions, and adjust the display layout. The technical implementation of the visualization system employs a single-page application (SPA) architecture that utilizes modern web technologies. Frameworks such as React.js, Vue.js, and Angular are used for the front end to achieve responsive design and cross-platform support. Data visualization libraries such as D3.js, Chart.js, Three.js, and Mapbox GL JS are combined to provide interactive and intuitive displays. Push technologies such as WebSocket, Server-Sent Events, and WebRTC are used for real-time updates, ensuring low-latency information updates. Geographical information display integrates OpenStreetMap, Google Maps API, and Leaflet.js to provide accurate location information and a wide range of map functions. Time-series data visualization uses time-series charting libraries (such as ApexCharts and Plotly.js) to enable zooming, panning, and time range selection. 3D visualization utilizes WebGL technologies such as Three.js and Babylon.js to provide a three-dimensional spatial representation and an immersive monitoring environment. Dashboard personalization will include features such as drag-and-drop layout changes, adding and removing widgets, and theme switching. Accessibility features will also be integrated, including color vision support, keyboard navigation, and screen reader support.

[0016] MODE FOR CARRYING OUT THE INVENTION (Prediction Function) In at least one embodiment, the prediction function predicts future behavior based on past patterns of occurrence, supporting the establishment of a proactive alert posture. Prediction algorithms can include time series analysis, regression analysis, machine learning, deep learning, and other methods, selected based on the required prediction accuracy and computational cost. Time series analysis analyzes temporal patterns such as seasonal fluctuations, periodicity, and trends to predict future occurrence probabilities. Machine learning learns the characteristics of behavioral patterns from past data to detect similar patterns and predict anomalous patterns. Prediction results are expressed in the form of probability distributions, confidence intervals, risk scores, and other formats, and provided as decision-making support information. The prediction period and granularity are configurable, allowing for flexible settings depending on the application, from short-term to long-term predictions. To continuously improve prediction accuracy, the system also includes a function to update the learning model by comparing it with actual detection results. A prediction correction function that takes into account the influence of external factors (weather, events, social conditions, etc.) provides more practical prediction results. The technical implementation of the forecasting system employs an ensemble learning approach that combines multiple time series forecasting methods. Statistical methods include ARIMA, SARIMA, exponential smoothing, and state space models to capture linear trends and seasonality. Machine learning methods include Random Forest, Gradient Boosting, and Support Vector Regression to learn nonlinear patterns. Deep learning methods include LSTM, GRU, Transformer, and Prophet to enable learning of complex long-term dependencies. Feature processing involves generating time-based features (day of the week, month, season, etc.), lag features, moving averages, and differences. External data integration incorporates external factors such as weather data, calendar data, and economic indicators as explanatory variables. Model evaluation uses evaluation metrics such as MAE, RMSE, MAPE, and quantile regression to perform a multifaceted evaluation of forecast performance. Bayesian deep learning, Monte Carlo dropout, and quantile regression are used to quantify forecast uncertainty, providing forecasts with confidence intervals. Online learning enables continuous model updating based on new data and addresses concept drift, while explainable AI technology improves visualization and interpretability of predictions.

[0017] MODE FOR CARRYING OUT THE INVENTION (EXTERNAL SYSTEM LINKAGE) In at least one embodiment, the system is equipped with a function for linking with external systems to achieve comprehensive security management. Linkage targets include security company monitoring centers, local government security systems, police department information systems, building management systems, and access control systems. Data linkage uses standard APIs, web services, database linkage, file transfer, and other methods, selecting the optimal linkage method based on the specifications of the partner system. To ensure security, the system implements functions such as data encryption, authentication and authorization, access control, and audit logs. Real-time linkage provides functions such as immediate reporting upon anomaly detection, live streaming of detected data, and instructions and confirmation via two-way communication. Periodic data synchronization using batch processing also enables efficient exchange of large amounts of data. Compatibility between different systems is ensured through functions such as protocol conversion, data format conversion, and character code conversion. High availability is achieved through functions such as linkage error detection and recovery and failover. For technical implementation of external system integration, we built a flexible integration platform that combines Service-Oriented Architecture (SOA) and microservices architecture. For API management, we used API gateways such as Kong, AWS API Gateway, and Azure API Management to implement rate limiting, authentication, logging, version control, etc. For messaging, we adopted high-availability message queues such as Apache Kafka, RabbitMQ, and Amazon SQS to ensure asynchronous communication and fault tolerance. For data conversion and routing, we utilized EIP patterns such as Apache Camel, Spring Integration, and Talend to efficiently implement complex integration flows. For security, we used standard protocols such as OAuth 2.0, JWT, SAML, and X.509 certificates to build a robust authentication and authorization system. For monitoring, we integrated Prometheus, Grafana, and ELK Stack to visualize integration status and detect anomalies. For failure response, we implemented resilience patterns such as the Circuit Breaker pattern, Bulkhead pattern, and Timeout pattern to prevent partial failures from spreading to the entire system. Additionally, to protect data privacy, we implement privacy-by-design practices such as data masking, pseudonymization, the principle of least privilege, and GDPR / CCPA compliance.

[0018] MODE FOR CARRYING OUT THE INVENTION (PRIVACY PROTECTION) In at least one embodiment, the system incorporates a privacy protection function to ensure appropriate management of personal information. Privacy protection features include facial image masking, anonymization of personally identifiable information, encrypted data storage, and access permission control. Facial image masking applies mosaic, blurring, blacking, and other processes to detected facial areas to make individuals difficult to identify. The masking strength can be configured to adjust the balance between surveillance purposes and privacy protection. Anonymization pseudonyms directly identifiable information such as name and address, and retains only information necessary for statistical analysis and behavioral pattern analysis. Data storage uses strong encryption technologies such as AES encryption and RSA encryption to protect data from unauthorized access. Access control prevents unauthorized access through user authentication, role-based access control, audit logs, and other methods. Other features include data retention period management in accordance with legal requirements and automatic data deletion. The technical implementation of the privacy protection system will include comprehensive protection functions based on the principles of Privacy by Design. Differential privacy technology mathematically controls the risk of individual identification while maintaining statistical usefulness. Homomorphic encryption technology enables statistical processing and machine learning to be performed on encrypted data without decrypting it. Zero-knowledge proofs provide the ability to prove the validity of data without disclosing its contents. Anonymization techniques such as K-anonymity, L-diversity, and T-closeness ensure resistance to re-identification attacks. Attribute-based access control (ABAC) enables fine-grained, context-dependent access control. Blockchain technology ensures tamper-proof and transparent data access history. Federated learning enables collaborative training of machine learning models without aggregating raw data. The data minimization principle ensures that only the minimum amount of data necessary for the purpose is collected and processed. The right to be forgotten will be implemented to automate data deletion at the request of individuals. Furthermore, automatic Privacy Impact Assessments (PIAs) will be integrated to assess privacy risks prior to the introduction of new features and implement appropriate countermeasures.

[0019] Mode for carrying out the invention (integration of various sensors) In at least one embodiment, the system integrates with various sensors to improve detection accuracy and reduce false alarms. Possible integrations include motion sensors, acoustic sensors, vibration sensors, temperature sensors, humidity sensors, smoke detectors, and gas detectors. Motion sensor integration combines detection signals from PIR sensors, microwave sensors, ultrasonic sensors, and other sensors with video analysis results to achieve more reliable person detection. Acoustic sensor integration analyzes acoustic information such as footsteps, voices, and other sounds to complement situations difficult to capture with video. Vibration sensor integration detects fence sway, door openings, and floor vibrations to support early detection of intrusions. Environmental sensor integration provides early warning of fires and chemical hazards by detecting abnormal temperature changes, smoke emissions, and hazardous gases. Sensor information fusion integrates and analyzes signals from each sensor to make comprehensive situational assessments. Time series analysis of sensor data improves the accuracy of detecting abnormal events and distinguishing between normal and abnormal conditions. In implementing the technology for a sensor integration system, we will build a distributed processing architecture utilizing an IoT platform and edge computing. For communication protocols, we will combine low-power, long-distance communication technologies such as MQTT, CoAP, LoRaWAN, Zigbee, and BLE to enable connection to a variety of sensors. For sensor data preprocessing, we will implement noise filtering, calibration, unit unification, and missing value imputation. For data fusion algorithms, we will use Kalman filters, particle filters, Dempster-Shafer theory, and Bayesian networks to integrate uncertain information from multiple sensors. For time series data analysis, we will apply anomaly detection algorithms (STL decomposition, LSTM autoencoder, isolation forest, etc.) to detect deviations from normal patterns in sensor data. Event correlation analysis will analyze the causal relationships between events occurring simultaneously on multiple sensors to reduce false alarms. For sensor status monitoring, we will use SNMP, OPC-UA, and dedicated protocols to detect sensor failures, monitor battery levels, and check communication status. In addition, Digital Twin technology allows for the virtual modeling of sensor placement in physical space, enabling optimization of sensor placement, blind spot analysis, coverage evaluation, and more.

[0020] Mode for carrying out the invention (utilizing AI technology) In at least one embodiment, the system is equipped with advanced analytical capabilities utilizing artificial intelligence technology. Applications of AI technology include improving image recognition accuracy, learning behavioral patterns, automating anomaly detection, and improving prediction accuracy. Deep learning technology uses techniques such as convolutional neural networks, recurrent neural networks, and transfer learning to build highly accurate recognition models from large amounts of training data. Reinforcement learning technology learns optimal monitoring strategies through interaction with the environment and enables dynamic adjustment of monitoring parameters. Natural language processing technology supports voice-based anomaly detection and monitoring in multinational environments with features such as speech recognition, text analysis, and multilingual support. In combination with edge computing technology, the system can selectively utilize local AI processing on the camera side and integrated AI analysis on a central server, thereby distributing processing load and improving responsiveness. Continuous learning and updating of AI models enables adaptation to environmental changes and new threats. Explainable AI technology visualizes the basis for AI decisions, improving system transparency and reliability. In the detailed implementation of AI technology, we will build a scalable AI system that integrates the latest deep learning architecture and MLOps pipeline. For computer vision, we will adopt state-of-the-art models such as Vision Transformer (ViT), EfficientNet, YOLOX, and DETR to achieve highly accurate object detection and classification. For time series analysis, we will use time series-specialized models such as Temporal Convolutional Network (TCN), Informer, and N-BEATS to enable learning of complex time dependencies. For natural language processing, we will utilize Transformer-based models such as BERT, GPT, and T5 to perform semantic understanding of speech recognition results and threat level assessment. For MLOps, we will use toolchains such as Kubeflow, MLflow, and DVC to automate model training, deployment, monitoring, and updates. For distributed learning, we will use frameworks such as Horovod, FairScale, and DeepSpeed ​​to enable efficient learning with large amounts of data. To reduce the model size, we apply technologies such as quantization, pruning, knowledge distillation, and Neural Architecture Search (NAS), enabling high-speed inference on edge devices. AutoML technology automates hyperparameter optimization, architecture search, feature selection, etc. Furthermore, federated learning enables collaborative learning in a distributed environment while protecting privacy, improving model performance without aggregating personal data.

[0021] MODE FOR CARRYING OUT THE INVENTION (Cloud Computing) In at least one embodiment, the system utilizes cloud computing technology to ensure scalability and availability. Cloud services are available at the IaaS, PaaS, and SaaS levels, allowing the optimal service type to be selected based on system requirements and cost constraints. Data storage uses object storage, block storage, file storage, and other storage types depending on the application, enabling efficient management of large volumes of data. Load balancing technology distributes processing across multiple server instances to ensure high throughput and availability. Auto-scaling functionality dynamically increases or decreases resources depending on processing load, optimizing the balance between cost-effectiveness and performance. Backup and recovery functionality minimizes the risk of data loss and ensures business continuity in the event of a disaster. Security features include protection against cloud-specific threats, geographic data distribution, and compliance support. Multi-cloud support allows for risk diversification by leveraging multiple cloud providers and avoids vendor lock-in. The technical details of the cloud implementation include adopting a cloud-native design based on microservices architecture and container technology. Container orchestration will be performed using Kubernetes, Amazon EKS, Google GKE, Azure AKS, etc., enabling automatic scaling, load balancing, health checks, rolling updates, etc. Serverless computing will be performed using AWS Lambda, Azure Functions, Google Cloud Functions, etc. for event-driven processing and cost optimization. The data lake will be created using Amazon S3, Azure Data Lake, Google Cloud Storage, etc., enabling integrated management of structured and unstructured data. Distributed databases will be created using Amazon DynamoDB, Azure Cosmos DB, Google Cloud Spanner, etc., ensuring high availability and consistency on a global scale. A CDN (Content Delivery Network) will enable fast delivery of video data and optimized bandwidth usage. Managed services such as AWS CloudWatch, Azure Monitor, and Google Cloud Operations will be integrated for monitoring and log management, enabling comprehensive system monitoring. Cloud security services such as IAM, VPC, encryption, WAF, and DDoS protection will be utilized to build a multi-layered defense for security. In addition, infrastructure as code (IaC) enables version management of infrastructure and the creation of reproducible environments using Terraform, CloudFormation, ARM Templates, etc.

[0022] MODE FOR CARRYING OUT THE INVENTION (COUNT OF OCCURRENCES) In at least one embodiment, a function for counting the number of repeated appearances of a person associates and records the identification information of a detected individual with the time of detection and automatically tallys the number of appearances of the same person within a specified period. This function combines multiple identification methods, such as facial recognition technology, walking pattern analysis, and clothing feature extraction, to achieve highly accurate individual identification, even when lighting conditions and angles change. The counting process can employ both real-time and batch processing, selectable depending on the system's processing power and responsiveness requirements. The aggregation period can be flexibly set to hours, days, weeks, months, etc., allowing for optimization based on monitoring objectives and security policies. A function for eliminating duplicate detections counts consecutive detections of the same person as a single appearance, providing accurate statistical data. The count data is persisted in a database and used for long-term trend analysis and statistical report generation. For the technical implementation of the counting function, we built an architecture that combines highly accurate person identification with efficient data processing. For real-time counting, we adopted a streaming processing infrastructure combining Apache Kafka and Apache Storm / Flink to achieve low-latency aggregation processing. To improve the accuracy of person identification, we integrated multimodal feature fusion (facial features + gait features + clothing features) using ensemble learning. For duplicate removal algorithms, we implemented time-windowed counting using a sliding window method, confidence-based integration decision, and state transition analysis using a hidden Markov model. For large-scale data processing, we utilized distributed processing frameworks such as Apache Spark and Hadoop MapReduce to enable real-time aggregation from millions of detection records. Time-series databases (InfluxDB, TimescaleDB) enabled fast time range queries and efficient data compression. For caching count results, we used in-memory data stores such as Redis Cluster and Memcached to ensure fast response to frequent access. For statistical processing, we utilized libraries such as Apache Arrow, Pandas, and NumPy to enable fast execution of complex aggregation queries. In addition, by utilizing approximate counting (HyperLogLog, Count-Min Sketch, etc.), it is possible to perform approximate aggregation on large-scale data while reducing memory usage, optimizing the trade-off between real-time performance and accuracy.

[0023] Mode for carrying out the invention (visit frequency abnormality warning) In at least one embodiment, the function for alerting users to abnormal visit frequency for a specific individual learns the visit patterns of each individual and automatically detects statistically abnormal occurrences. This function learns the normal visit patterns for each individual from past visit data and calculates statistical indicators such as average visit frequency, standard deviation, and seasonal variation. To determine abnormalities, algorithms such as outlier detection, change point detection, and time series anomaly detection are used to detect sudden increases in frequency or abnormal patterns. Threshold settings can be selected from fixed value methods, learning-based methods, administrator settings, and other methods, allowing for flexible adjustments according to the operating environment. Multiple indicators can be set as alert conditions, such as absolute frequency (e.g., more than five times per day), relative frequency (e.g., more than three times the historical average), and cumulative frequency (e.g., more than 20 times per week). Detected anomalies are recorded along with a reliability score to reduce false alarms and improve accuracy. The alert information includes detailed information such as the target user's identification information, the degree of abnormality, and estimated behavioral patterns. In the technical implementation of the visit frequency anomaly detection system, we build a multi-layered anomaly detection architecture that combines statistical learning and machine learning. For basic statistical analysis, we use moving averages, exponential smoothing, and seasonal decomposition (STL: Seasonal and Trend decomposition using Loess) to perform baseline learning of individual visit patterns. For anomaly detection, we combine unsupervised learning methods such as One-Class SVM, Isolation Forest, Local Outlier Factor (LOF), and Robust Covariance estimation. For time series anomaly detection, we employ deep learning models such as LSTM Autoencoder, Temporal Convolutional Network (TCN), and Prophet to learn complex time dependencies. For change point detection, we use methods such as Cumulative Sum (CUSUM) statistics, Page-Hinkley test, and Bayesian Online Changepoint Detection to detect sudden changes in visit patterns. For personalized learning, we utilize technologies such as Meta-Learning and Few-Shot Learning to build an efficient personalized model from small amounts of personal data. Dynamic adjustment of thresholds uses ROC curve optimization, precision-recall curve analysis, cost-sensitive learning, etc. to optimize the balance between false alarm rate and detection rate. In addition, explainable AI (SHAP, LIME) technology is used to visualize the basis for anomaly detection, integrating a function to support decision-making by security personnel.

[0024] Mode for carrying out the invention (identifying revisits with the same clothes and appearance) In at least one embodiment, the function for identifying repeat visits with the same clothing and appearance learns and records a person's appearance characteristics and detects multiple appearances with similar appearances. This function extracts visual features such as clothing color, pattern, shape, and texture using image analysis and quantifies them as feature vectors. Appearance feature extraction utilizes a combination of technologies, including hue, saturation, and brightness analysis, texture analysis, shape recognition, and fashion item detection. Similarity determination can employ methods such as distance calculation between feature vectors, machine learning classification, and deep learning feature matching. Time-series changes in appearance are taken into account, and factors such as seasonal variations, weather-related clothing changes, and the addition or removal of accessories are incorporated into the learning process. Repeat visit detection with the same appearance counts the number of similar appearances within a specified period and issues a warning if the same appearance appears abnormally frequently. The appearance database stores only anonymized feature information to ensure privacy. Detection results are recorded along with similarity scores, allowing for manual review to improve accuracy. In the technical implementation of an appearance feature analysis system, we will build a highly accurate clothing and appearance recognition system using the latest computer vision technology and deep learning. Clothing analysis will use a Fashion-MNIST-based classifier, a segmentation model trained on the DeepFashion2 dataset, and dedicated architectures such as FashionNet. Color analysis will implement hue distribution analysis in HSV color space, perceptual color difference calculation in LAB color space, and a palette extraction algorithm (K-means clustering). Texture analysis will combine traditional methods such as Local Binary Pattern (LBP), Gray-Level Co-occurrence Matrix (GLCM), and Gabor Filter Bank with deep learning methods such as Texture CNN and Scattering Transform. Shape recognition will apply contour extraction techniques such as Contour Detection, Hough Transform, and Active Contour Model. Feature matching will use metric learning methods such as Siamese Network, Triplet Network, and ArcFace to improve the accuracy of similarity calculations. To handle time-series changes, we use Temporal Convolutional Networks, LSTM, attention mechanisms, etc. to learn patterns of temporal changes in appearance. Furthermore, data augmentation (color transformation, lighting changes, partial occlusion, etc.) improves robustness against various shooting conditions. To protect privacy, we implement techniques such as differential privacy, k-anonymity, and feature vector anonymization to minimize the possibility of identifying individuals.

[0025] MODE FOR CARRYING OUT THE INVENTION (Facial Recognition-Based Personal Revisit Record) In at least one embodiment, a facial recognition-based personal revisit record function identifies individuals using highly accurate facial recognition technology and records and manages their revisit history in detail. This function uses a deep learning-based facial recognition algorithm to perform a series of processes, including facial feature point extraction, normalization, feature vector generation, and similarity calculation. The facial recognition process supports both real-time and batch recognition, selecting the optimal method depending on the processing load and responsiveness requirements. A facial image quality improvement function achieves highly accurate recognition even under difficult conditions such as low-resolution images, partial occlusion, and lighting changes. Individual facial features are stored in an encrypted database and protected from unauthorized access. The revisit record includes detailed information such as visit date and time, length of stay, detection location, and confidence score, creating a comprehensive behavioral history. To ensure privacy, the function implements protection features such as automatic facial image deletion, anonymization processing, and access permission control. To continuously improve recognition accuracy, the function includes functions such as corrective learning for misrecognitions and additional learning for new registered users. In the technical implementation of the face recognition system, we build a high-performance face recognition platform that integrates the latest deep learning architecture and advanced image processing technology. For face detection, we use high-precision detectors such as MTCNN, RetinaFace, and DSFD (Dual Shot Face Detection) to achieve robust detection under various angles, distances, and lighting conditions. For facial feature extraction, we employ metric-based embedding learning methods such as ArcFace, CosFace, and SphereFace to learn effective face representations in high-dimensional feature spaces. Face preprocessing involves face alignment (68-point / 98-point landmark detection), normalization (standardization to 112x112 or 224x224), and quality assessment (evaluation of blur, lighting, and angle). To improve recognition accuracy, we combine techniques such as Test Time Augmentation (TTA), Multi-Scale Detection, and Template Matching. For face database management, we use approximate nearest neighbor search libraries such as Faiss, Annoy, and NMSLIB, which enable high-dimensional vector search. For privacy protection, cryptographic techniques such as Template Protection (BioCryptoSystems), Homomorphic Encryption, and Secure Multi-party Computation are implemented. For real-time processing, inference optimization frameworks such as TensorRT, OpenVINO, and ONNX Runtime are utilized to achieve high-speed execution on GPU / CPU / TPU. In addition, technologies such as Continual Learning and Meta-Learning are implemented to implement functions that enable both efficient learning of new face data and knowledge retention of existing models.

[0026] In at least one embodiment, the vehicle appearance frequency tracking function by license plate uses optical character recognition technology to identify vehicles and continuously monitor their appearance patterns. This function automatically performs a series of processes: license plate detection, character region extraction, character recognition, and vehicle matching. License plate detection uses a machine learning-based object detection algorithm to detect plates at various angles, distances, and lighting conditions. Character recognition processing combines OCR technology with deep learning to accurately read characters even on dirty, damaged, or partially obscured plates. Vehicle appearance frequency is aggregated by period, such as daily, weekly, or monthly, and abnormal appearance patterns are detected through statistical analysis. Additional information, such as area code, vehicle type classification, and registration year, is also recorded to enable more detailed vehicle profiling. Vehicle tracking at multiple monitoring locations allows for estimation of travel routes and detection of patrol behavior. The vehicle database includes classification functions such as blacklists and whitelists to support automatic identification of vehicles under surveillance. To protect privacy, vehicle owners' personal information is not stored; only license plate information is anonymized and managed. Enhanced Technical Details: License plate detection uses YOLOv5 and SSD-based detectors, achieving a detection rate of over 95% while maintaining a processing speed of over 30 fps per frame. Character recognition employs a Convolutional Recurrent Neural Network (CRNN) architecture, and sequential character string recognition maintains high recognition accuracy even for faded or deformed plates. Vehicle attribute analysis combines color analysis in hue-saturation-value (HSV) space, vehicle classification using HOG features, and geometric measurement of vehicle size. Moving object tracking utilizes Kalman and particle filters to match vehicles across multiple frames and predict their trajectories. To improve recognition at night and in poor weather conditions, the system implements infrared illumination linkage, image quality correction using a rain sensor, and a fog removal algorithm. Counterfeit license plate detection incorporates matching against known regular patterns and font analysis to determine authenticity.

[0027] In at least one embodiment, the time-of-day appearance frequency recording function performs detailed analysis of the appearance times of detected people and vehicles to understand temporal patterns and detect anomalies. This function divides 24 hours into arbitrary time units (e.g., 30 minutes, 1 hour, 2 hours) and aggregates the number of appearances in each time period. Time data recording includes functions such as accurate recording of detection times, time zone compatibility, and automatic daylight saving time adjustment. Statistical analysis includes the average number of appearances by time period, identification of peak times, and detection of appearances during abnormal times. A dynamic baseline adjustment function is implemented to account for fluctuations in time patterns due to day of the week and season. Visualization of time-of-day data is displayed in the form of heat maps, time series graphs, radar charts, and other formats to help intuitively understand patterns. Higher alert levels can be set for appearances during special times, such as outside business hours, late nights, and early mornings. Time-of-day warning settings enable immediate alerts for abnormal appearances during specific time periods. Long-term data storage also enables analysis of long-term time patterns, such as seasonal and annual fluctuations. Enhanced Technical Details: Time series data analysis utilizes the ARIMA (Autoregressive Integrated Moving Average) model, LSTM (Long Short-Term Memory) network, and the Prophet time series forecasting library to achieve highly accurate anomaly detection that takes seasonality and trends into account. The peak detection algorithm combines smoothing using a Savitzky-Golay filter with local maxima detection to achieve noise-resistant peak finding. The dynamic time-of-day segmentation function automatically identifies business hours, break times, maintenance times, etc. based on the facility's operational status. Statistical outlier detection (IQR method, Z-score) and machine learning-based anomaly detection (Isolation Forest, One-Class SVM) are used to calculate anomaly scores, enabling multifaceted anomaly detection. Time correlation analysis quantifies the impact of occurrences in specific time periods on other time periods and identifies chained behavioral patterns. Integration with a holiday and special event calendar properly handles changes in non-stationary time patterns.

[0028] In at least one embodiment, the function for aggregating the occurrence of specific targets by day of the week analyzes behavioral patterns on a weekly basis and detects anomalies specific to each day of the week. This function records the number of occurrences for each day of the week from Monday to Sunday separately and generates statistical data for each day of the week. Flexible settings are possible, such as distinguishing between weekdays and holidays, treating holidays specially, and handling holidays specific to each region. Analysis by day of the week calculates statistical indicators such as the average number of occurrences for each day of the week, standard deviation, maximum and minimum values, and uses them to set normal ranges. Anomaly detection detects sudden increases in occurrences on specific days of the week, occurrences on unusual days of the week, and abnormal patterns on consecutive days of the week. The system is equipped with an analysis function that takes into account social factors such as work patterns, school schedules, and business patterns of commercial facilities. Visualization of data by day of the week supports understanding weekly patterns by displaying them in bar graphs, radar charts, calendar formats, etc. Trend analysis over multiple weeks makes it possible to distinguish between stable patterns and fluctuating factors. Day-of-week alert settings issue appropriate levels of alerts for abnormal occurrences on specific days of the week. By accumulating long-term data, detailed analysis of day-of-the-week patterns, including seasonal and annual variations, can be performed. Enhanced technical details: For weekday pattern analysis, periodicity detection algorithms (FFT, autocorrelation) are applied to quantitatively evaluate 7-day cycles for regularity and exception patterns. To take into account the influence of social factors, the system connects with external calendar APIs (Google Calendar API, Microsoft Graph API) to automatically obtain information on local holidays, events, school holidays, etc. For setting reference values ​​for each day of the week, an adaptive threshold update function using Bayesian statistics is implemented, improving judgment accuracy based on accumulated past data. The business logic embedding function learns industry-specific patterns, such as "low customer traffic on Mondays" and "frequent late hours on Fridays due to overtime work," in the retail industry. Day-of-week correlation analysis discovers continuous patterns, such as "visitors on Tuesdays tend to also appear on Wednesdays," improving prediction accuracy. The anomaly detection sensitivity adjustment function optimizes the balance between false positives and false negatives according to business requirements.

[0029] In at least one embodiment, the function for listing long-stayers in a specific area divides the monitored area into multiple zones and accurately measures the time spent in each zone. This function automatically records entry and exit times, tracks movement trajectories, and calculates stay time to identify stagnants who exceed a set time threshold. Zone settings can be flexibly defined, including physical boundaries, virtual boundaries, and overlapping zones, and can be optimized according to the facility layout and monitoring objectives. The calculation of stay time uses multiple indicators, such as continuous stay time, cumulative stay time, and effective stay time, to achieve a more accurate understanding of stay status. A function is provided to exclude temporary departures that involve movement and count only actual stays. Criteria for determining long stays can be individually set by zone, time period, target attributes, etc. The visitor list displays detailed information such as the start time of stay, current stay time, expected stay time, and estimated purpose of stay. A tiered warning system supports timely intervention in the event of abnormal long stays. Statistical analysis of stay data can distinguish normal from abnormal stay patterns. Enhanced Technical Details: The area management system utilizes GIS (geographic information system) technology to achieve precise location management by integrating latitude and longitude coordinates, building floor information, and a 3D spatial coordinate system. The dwell time calculation algorithm combines location estimation using a Kalman filter and behavioral state estimation using a Bayesian network to accurately determine dwell time even with noisy detection data. By linking with IoT sensors (PIR, ultrasonic, and pressure sensors), the system can detect people in camera blind spots, improving the accuracy of dwell time calculations. A machine learning-based behavior classification function is implemented to automatically estimate the purpose of stay, such as "waiting," "working," "resting," and "wandering." Abnormal dwell patterns are categorized into wandering (irregular movement within the same area), hiding (extended stays in hidden locations), and surveillance (continuous observation of specific locations). A real-time heatmap generation function visualizes the dwell density distribution throughout the facility and helps optimize security guard patrol routes.

[0030] In at least one embodiment, the continuous detection feature immediately triggers an alert by detecting repeated occurrences within a short period of time and issuing a high-level warning. This feature monitors the number of detections within a specified time period (e.g., 1 hour, 30 minutes, or 15 minutes) in real time and immediately triggers an alert when a preset threshold (e.g., 3, 5, or 10 times) is exceeded. Time window settings can be selected from fixed, sliding, or adaptive window methods, allowing for optimization according to the monitoring objective. Continuous detection determination utilizes a combination of multiple identification methods to improve the accuracy of identifying the same person or vehicle. To reduce false positives, features include setting a minimum detection interval, removing duplicate detections at the same location, and filtering by reliability score. When an alert is triggered, detailed data such as detection history, time interval, detection location, and target information is automatically collected and provided as information to support situation assessment. Urgency levels can be set to enable tiered warnings based on the number of continuous detections. Multiple notification methods (e.g., email, SMS, voice, and screen display) are combined to ensure reliable warning delivery. The continuous detection pattern learning function improves the accuracy of distinguishing between normal and abnormal continuous occurrences. Enhanced technical details: The continuous detection and judgment algorithm combines sliding window technology and event stream processing (ESP) to achieve high-speed pattern matching in real time. To improve the accuracy of identifying the same person, multi-modal recognition (face + clothing + gait + body type) is implemented, and the reliability of each modality is integrated using weighted voting. For time series anomaly detection, normal pattern learning using LSTM-Autoencoder and anomaly level calculation based on reconstruction error are applied. The adaptive threshold function sets dynamic warning criteria taking into account time of day, day of the week, and seasonal factors to minimize false alarms. The warning escalation function applies time interval settings based on the Fibonacci sequence (1 minute → 1 minute → 2 minutes → 3 minutes → 5 minutes...) to promote gradual response according to the urgency. A clustering function that takes geographic proximity into account integrates simultaneous detections from multiple locations, enabling early detection of organized behavior.

[0031] In at least one embodiment, a cross-camera person appearance counting function common to all cameras integrates multiple surveillance cameras to enable comprehensive appearance monitoring throughout the entire facility. This function consolidates detection data from each camera on a central server, and integrates and manages detections of the same person across different cameras. Person matching across cameras combines multiple methods, including facial recognition, walking pattern analysis, and clothing feature matching, to achieve highly accurate person identification. The time synchronization function enables accurate recording of detection timing across multiple cameras and estimation of movement trajectories. Dynamic system configuration, which accommodates the addition and removal of cameras, supports flexible expansion and modification of the monitoring area. Network load balancing ensures stable operation even in large-scale camera networks. Cross-camera count data visualization provides comprehensive information, such as displaying people's movements on a floor map of the entire facility, detection statistics by camera, and movement history by person. Anomaly detection detects complex behavioral patterns, such as unusual movement patterns, concentration in specific areas, and evasive behavior. Integrated data management enables behavioral analysis over a wide area that cannot be captured by a single camera, providing more effective security surveillance. Enhanced technical details: The distributed computing architecture uses Apache Kafka, Apache Storm, and Redis Cluster to build a real-time streaming processing infrastructure that processes thousands of detection events per second with low latency. For multi-camera person matching, a graph neural network (GNN) is applied, incorporating spatial and temporal relationships between cameras into the learning model to improve matching accuracy. The camera calibration function automatically calculates the intrinsic and extrinsic parameters of each camera, enabling accurate position estimation in 3D space. The load balancing algorithm combines consistent hashing and load balancing to minimize data relocation when adding cameras. The data deduplication function implements a bloom filter and counting bloom filter to perform memory-efficient duplicate checks. For real-time trajectory prediction, a Kalman filter and particle filter are used in combination to maintain robust tracking even in the face of occlusions and temporary detection failures.

[0032] In at least one embodiment, the immediate notification function when a specified number of detections is exceeded sends emergency notifications to relevant parties via various communication methods when a set detection count threshold is exceeded. This function simultaneously utilizes multiple notification channels, such as email, SMS, mobile app push notifications, voice calls, and chat apps, to ensure reliable information transmission. Notification content includes detailed information such as the target's identification information, number of detections, detection time, detection location, and image data, supporting rapid situation assessment. Notification recipients can be set at multiple levels, such as security managers, administrators, security companies, and relevant departments, and an escalation function enables tiered notification. Notification priorities can be set according to urgency, allowing for priority delivery of important alerts. The notification confirmation and response function manages the recipient's response status and automatically re-notifies unaddressed alerts. Notification history is recorded to analyze response times, evaluate notification effectiveness, and accumulate data for system improvement. Time-based notification functions, such as automatic notification during nighttime and holidays and special response outside of business hours, are included. High availability is ensured by automatically switching to a backup notification route in the event of a communication failure. Enhanced technical details: The notification delivery system uses message queues (Apache ActiveMQ, RabbitMQ) and a pub-sub pattern to ensure high availability and scalability. It integrates the Twilio API for SMS delivery, and Firebase Cloud Messaging (FCM) and Apple Push Notification Service (APNS) for push notifications, achieving cross-platform support. It implements a priority queue algorithm and exponential backoff for notification priority management, controlling delivery order and retransmission based on importance. It uses Apache Velocity and Freemarker for the notification template engine, enabling dynamic notification content generation and multilingual support. It implements delivery receipts, read receipts, and acknowledgment mechanisms to confirm notification delivery, ensuring reliable information transmission. It combines circuit breaker patterns and health check functions to automatically bypass abnormal communication paths in response to communication failures. It uses AES-256 to encrypt notification content and TLS 1.3 to protect communication paths, ensuring thorough protection of confidential information.

[0033] In at least one embodiment, the automatic tracking registration function for targets exceeding a threshold automatically registers people or vehicles whose detection count exceeds a set standard as targets requiring monitoring, establishing an enhanced monitoring system. This function automatically updates the tracking target list, sets tracking levels incrementally, and manages tracking periods. During tracking registration, a comprehensive profile is generated, including the target's basic information, detection history, behavioral patterns, and risk score. Tracking levels can be set to various levels, such as careful monitoring, strict monitoring, and maximum vigilance, and are automatically adjusted based on the number of detections and behavioral patterns. The automatic tracking function prioritizes detection of the appearance of registered targets and continuously accumulates detailed behavioral records. Tracking target behavior analysis performs multifaceted analysis of movement patterns, locations, visit frequency, time trends, etc. An automatic tracking cancellation function removes targets from the tracking list at the appropriate time once they have returned to normal behavioral patterns. A manual tracking registration / cancellation function is also provided, allowing flexible response at the administrator's discretion. Tracking target data is encrypted and stored to appropriately protect highly confidential monitoring information. Compliance functions such as tracking record retention period management and data deletion are implemented in accordance with legal requirements. Enhanced Technical Details: The automatic tracking system applies reinforcement learning to learn optimal tracking criteria from past tracking results and manual correction history. Ensemble learning (Random Forest, Gradient Boosting, Neural Network) is used to calculate risk scores and perform comprehensive threat assessments from multidimensional data. Markov Chain Monte Carlo (MCMC) methods are applied to predict the behavior of tracked targets, enabling probabilistic behavioral pattern prediction and confidence interval calculation. Change point detection algorithms (CUSUM, PELT) are used to objectively detect statistical change points in behavioral patterns to determine whether to stop automatic tracking. A graph database is used for the tracking database to efficiently manage and analyze relationships, collusion, and group structure between tracked targets. Differential privacy technology is implemented as a privacy protection function, making it difficult to identify individuals while retaining the information necessary for statistical analysis. API integration functionality enables secure sharing of tracking information with external systems (security companies, law enforcement agencies).

[0034] In at least one embodiment, an automatic notification feature for security guard mobile devices instantly delivers alert information to mobile devices to support rapid response in the field. This feature supports a variety of devices, including smartphones, tablets, and dedicated communication devices, providing notifications optimized for the security guard's work environment. The mobile application integrates functions such as alert reception, situation confirmation, response recording, and location sharing to support efficient security activities in the field. Integration with GPS displays the relationship between the guard's current location and the location of the alert and suggests the optimal response route. The push notification function ensures that alerts are received even when the app is inactive. Alert information includes information necessary for on-site decision-making, such as an image of the target, a map of the detection location, past detection history, and recommended response procedures. The voice readout function allows the content of alerts to be confirmed while on the move or while working. Offline functionality enables basic alert reception and recording even in environments with unstable communication conditions. Multilingual support supports operations in international security systems. Information sharing between security guards enables coordinated response across the entire team. Optimized battery efficiency ensures stable operation even during long periods of security work. Enhanced Technical Details: The mobile application uses cross-platform development frameworks such as React Native and Flutter, supporting both iOS and Android for a unified user experience. The location information service integrates multiple satellite positioning systems, including GPS, GLONASS, Galileo, and BeiDou, to achieve highly accurate positioning both indoors and outdoors. Offline functionality utilizes an embedded SQLite database and local storage to continue saving and displaying important warning information even during communication disruptions. Security features include application-level encryption (AES-256), biometric authentication (fingerprint and face recognition), and certificate pinning to prevent unauthorized access. Real-time communication uses WebSocket and Server-Sent Events (SSE) for low-latency, two-way communication. Battery optimization includes power-saving features such as background processing limits, adaptive communication frequency adjustment, and automatic screen brightness adjustment. Augmented reality (AR) functionality overlays warning information on camera footage, providing intuitive situational awareness.

[0035] In at least one embodiment, the audio alert and PATLITE® linkage function provides visual and audible warnings to immediately alert and deter intruders. This function automatically plays audio messages, generates warning sounds, and controls the on / off flashing of rotating lights in response to detected abnormalities. The audio system includes pre-recorded messages, real-time voice synthesis, multilingual support, and automatic volume adjustment. Audio content can be selected to suit the situation, from general warnings to specific instructions. The PATLITE® system supports a variety of light sources, including LED, xenon, and rotating types, and controls color, brightness, and flashing patterns according to the alert level. Audio and light linkage patterns can be individually configured by urgency, time of day, location, etc. A neighborhood consideration function automatically adjusts the volume and restricts the direction of light at night or in residential areas. Sensor linkage activates an alert only when approaching people is detected, achieving both energy savings and effective deterrence. Warning history recording allows for continuous validation and optimization of effectiveness. By linking with an external security system, a more comprehensive warning and intimidation system can be created. Enhanced Technical Details: The speech synthesis system integrates deep learning-based Text-to-Speech (TTS) engines (Google WaveNet, Amazon Polly, and Microsoft Speech Platform) to generate natural-sounding messages in real time. The acoustic signal processing features adaptive noise cancellation, acoustic echo cancellation, and automatic gain control to ensure clear speech output that adapts to environmental noise and reverberation. The light control system features PWM (pulse-width modulation) control, an RGB LED matrix, and DMX512 protocol support to enable complex lighting patterns and color control. Audio and light synchronization control utilizes multimedia synchronization protocols (PTP and NTP) to maintain precise synchronization at the millisecond level. The environmental adaptation function automatically adjusts to environmental conditions by linking with ambient light sensors, sound level meters, and weather sensors. The IoT integration function enables integrated control with smart building systems, disaster prevention systems, access control systems, and more. Power-saving control utilizes motion sensing and occupancy detection to optimize energy efficiency by operating only when necessary.

[0036] In at least one embodiment, the notification priority leveling function creates a tiered notification system according to the urgency of the alert, supporting an efficient response system. This function promotes appropriate responses according to the level of importance by setting multiple priority levels, such as emergency, alert, caution, and information. Automatic priority determination uses a machine learning algorithm that comprehensively evaluates multiple factors, such as the number of detections, time patterns, location, and the target's past history. Different notification methods, notification recipients, and response time requirements can be set for each priority level, enabling operations tailored to the organization's security system. The escalation function automatically elevates the priority of low-priority alerts over time if they are not addressed. Notification method optimization implements tiered delivery, such as immediate voice call for emergency alerts, SMS and email for alert alerts, and email only for caution alerts. Statistical analysis by priority level accumulates data such as alert distribution trends, response times, and effectiveness measurements, supporting continuous system improvement. Administrators can manually adjust priorities to flexibly respond to special situations and policy changes. The priority setting learning function builds an optimal priority determination model based on past response performance. Enhanced technical details: The priority determination system combines multi-criteria decision analysis (MCDA) and fuzzy logic to quantify complex decision criteria, including ambiguity. The machine learning model utilizes gradient boosting (XGBoost, LightGBM) and neural network ensembles to achieve highly accurate priority prediction in high-dimensional feature space. Dynamic priority adjustment employs reinforcement learning with human feedback (RLHF), utilizing manual corrections by administrators as training data. Escalation control employs an exponential backoff with jitter algorithm to achieve gradual priority improvement while distributing system load. Notification delivery optimization employs queuing theory and load balancing to ensure stable delivery even when a large number of simultaneous alerts are generated. The statistical analysis dashboard implements real-time metrics (Grafana, Kibana) to monitor key performance indicators (KPIs) for each priority in real time. A feedback loop function continuously evaluates the appropriateness of response results and priorities, improving the accuracy of the predictive model.

[0037] In at least one embodiment, the group alert function for multiple simultaneous threshold violations detects group threats when multiple individuals exhibit anomalous behavior at the same time and issues a special alert. This function integrates and analyzes anomaly detections of multiple individuals related in time and space to evaluate the possibility of organized behavior or coordinated suspicious activity. Group formation is determined using indicators such as simultaneous appearance, similar behavioral patterns, coordinated movement, and role division to distinguish from solo behavior. The group threat level is calculated based on group size, membership, behavioral patterns, duration, etc. Advanced analysis, such as leader-follower identification, decision-making structure estimation, and behavior prediction, is performed through analysis of the interrelationships between multiple individuals. Group alerts set notification content, response procedures, and escalation routes different from individual alerts, and activate a security system specialized for group response. Detection of geographically dispersed group activity enables early detection of coordinated behavior and distributed threats at multiple locations. Similarities with known organizations and methods are evaluated by comparing with a database of past group activity. Automatic detection of group disbandment allows appropriate reduction of the threat level and management of the return to normal monitoring. Liaison functions with law enforcement agencies will assist in the appropriate reporting and collaboration of suspected organized crime. Enhanced technical details: The group detection algorithm combines graph neural networks (GNN) and community detection algorithms (Louvain and Leiden) to track dynamic group formation and dissolution over time. Spatial correlation analysis applies spatial clustering (DBSCAN and OPTICS) and spatiotemporal data mining techniques to detect geographically distributed cooperative behavior. Behavioral synchronization analysis uses cross-correlation analysis, dynamic time warping (DTW), and mutual information to quantify the similarity of behavioral patterns between different individuals. By incorporating a group psychology model, group-specific behavioral principles such as herding, conformity effects, and social facilitation are utilized for prediction. For real-time network analysis, streaming graph processing frameworks (Apache Giraph and GraphX) are utilized to enable dynamic analysis of large-scale group relationships. For threat level calculation, Monte Carlo simulation is applied to perform probabilistic assessment of complex threat factors, including uncertainty.

[0038] In at least one embodiment, a patrol behavior detection function that integrates detection at multiple locations integrates and analyzes data obtained from a wide-area monitoring network to detect organized reconnaissance activities and planned patrol behavior. This function analyzes detection patterns of the same target at geographically dispersed monitoring locations over time to evaluate the possibility of planned movement or reconnaissance. Patrol pattern recognition utilizes a combination of techniques, such as analysis of movement paths, evaluation of stay times, analysis of visit sequences, and detection of periodicity. The degree of planning and organization is quantified and expressed as a threat level based on appearance patterns across multiple facilities, multiple areas, and multiple time periods. A transportation estimation function identifies travel methods, such as walking, cycling, and driving, and uses this information to evaluate the range and planning of activities. Patrol behavior purpose estimation uses machine learning to classify the possibility of reconnaissance, reconnaissance, surveillance, intelligence gathering, etc. Abnormal patrol pattern detection distinguishes between normal patrols, such as regular business patrols, delivery work, and cleaning work. A patrol route prediction function estimates the next visit location and time, supporting the establishment of a proactive surveillance system. By detecting long-term patrol patterns spanning multiple days and weeks, early signs of planned crimes can be detected. Enhanced technical details: For travel behavior analysis, Hidden Markov Models (HMM) and Random Walk theory are combined to achieve probabilistic travel pattern modeling and state transition prediction. For geographic network analysis, graph theory, shortest path algorithms (Dijkstra, A*), and network centrality measures are utilized to identify strategically important locations. For spatiotemporal data mining, ST-DBSCAN (Spatio-Temporal DBSCAN), trajectory clustering, and pattern mining algorithms are applied to extract meaningful patterns from large amounts of travel data. For travel mode estimation, speed and acceleration analysis of GPS trajectories, stop-and-go pattern analysis, and road network matching (map matching) are combined. For purpose classification using machine learning, feature engineering (travel distance, stay time, visit frequency, time of day, etc.) and ensemble methods are utilized to achieve highly accurate behavioral purpose estimation. The forecasting function combines time series forecasting (LSTM, Prophet) and Monte Carlo methods to provide probabilistic predictions of future behavior, including uncertainty.

[0039] In at least one embodiment, the early detection alert function, which takes into account off-site influences, expands the monitoring range beyond the site boundaries, enabling early detection of abnormal behavior over a wide area. This function also monitors behavior on public roads, adjacent properties, and surrounding facilities, detecting premonitory behavior before intrusion into the site. Off-site monitoring, taking privacy into consideration, targets only behavior in public spaces and excludes monitoring of private property and residential areas. Premonitory behavior detection items include loitering around the site, observing boundaries, investigating intrusion routes, and checking security conditions. By linking with a geographic information system, geographic information such as site boundaries, public roads, and building layouts can be accurately identified, allowing for the establishment of an appropriate monitoring range. By combining multiple monitoring technologies (cameras, sensors, drones, etc.), wide-area monitoring without blind spots can be achieved. Time-series analysis of early detection predicts the time until an intrusion occurs, supporting the planning of preventative measures. Information sharing with neighboring facilities allows for the creation of a coordinated crime prevention system across the entire region. The system's ability to comply with legal restrictions ensures the legality of the surveillance scope and prevents violations of privacy rights. Accumulating data on early detection allows for long-term analysis of local crime trends and changes in methods, which can be used to improve crime prevention measures. Enhanced technical details: The off-site surveillance system combines long-range surveillance cameras (PTZ, thermal imaging, night vision) with edge computing to achieve wide-area, real-time monitoring. Privacy protection features include automatic face blurring, license plate masking, and selective area monitoring to ensure compliance with legal requirements. Geographic information integration utilizes GIS software (QGIS, ArcGIS), OpenStreetMap API, and satellite imagery analysis to integrate detailed geographic context into the surveillance system. Predictive behavior pattern learning utilizes sequence mining, temporal pattern mining, and anomaly detection in time series to automatically learn typical pre-intrusion behaviors from past data. Multi-sensor fusion uses Kalman filtering, particle filtering, and Dempster-Shafer theory to integrate information from different sensors for reliable detection. Legal compliance features include privacy impact assessment (PIA), data protection by design, and a consent management system to ensure compliance with personal information protection laws.

[0040] In at least one embodiment, the occurrence frequency ranking display function ranks and displays detected people and vehicles by frequency of appearance, supporting the determination of monitoring priorities. This function automatically generates a ranking based on the number of appearances within a specified period and displays them in order from top to bottom. The ranking display also includes detailed information such as the number of appearances, most recent detection date and time, first detection date and time, detection interval, and risk score. Period settings can be selected from daily, weekly, monthly, and custom periods, allowing for flexible aggregation according to analytical purposes. The ranking filtering function enables display of narrowed down results based on specific conditions (time of day, location, attributes, etc.). Multiple sorting criteria (number of appearances, most recent detection, risk score, etc.) are provided to meet the needs of monitors. The ranking change tracking function monitors the rise and fall of rankings to detect the emergence of new threats and changes in existing threats. The automatic tracking function for top-ranked individuals continuously monitors those who frequently appear. The ranking data export function supports analysis and report creation in external systems. Visual indicators (color coding, icons, warning marks, etc.) allow for an intuitive understanding of risk levels. Enhanced technical details: The ranking system utilizes distributed computing frameworks (Apache Spark, Hadoop MapReduce) to enable high-speed aggregation processing of large-scale datasets. Streaming analytics (Apache Kafka Streams, Apache Flink) are implemented for real-time update functionality, allowing new detected data to be reflected immediately. Statistical significance tests (Mann-Whitney U test, Kolmogorov-Smirnov test) are incorporated to evaluate the statistical significance of ranking fluctuations. Anomaly detection using machine learning (Isolation Forest, Local Outlier Factor) automatically identifies patterns that differ significantly from the norm. Caching functions (Redis, Memcached) enable high-speed response for frequently accessed ranking data. Interactive charting libraries (D3.js, Chart.js, Plotly) are utilized for data visualization, providing dynamic and intuitive ranking displays. Performance optimization includes database indexing, query optimization, and data partitioning to enable high-speed searches of large amounts of data.

[0041] In at least one embodiment, a list display function with thumbnail images of frequently detected targets speeds up visual identification of frequently occurring targets and supports efficient surveillance operations. This function automatically extracts facial images and vehicle images of detected individuals and selects and displays the highest quality images as thumbnails. Automatic image quality evaluation comprehensively assesses resolution, brightness, angle, clarity, etc. to select the optimal representative image. It also has a function to integrate images obtained from multiple detections to generate clearer, more distinctive composite images. A privacy protection function applies facial masking and blurring as needed. Important information, such as the number of detections, the most recent detection date and time, and the risk level, is overlaid on the thumbnail images. A detailed display function that can be clicked on an image provides comprehensive information, such as a full-size image, detection history, and behavioral patterns. A customizable list display function allows users to adjust the display items, sort order, filter conditions, and other settings according to their needs. An automatic image update function automatically replaces images with better quality images obtained from new detections. Displaying images from multiple angles helps users understand the multifaceted characteristics of targets. By optimizing the compression of image data, high-speed rendering is achieved even when displaying a large number of thumbnails. Enhanced Technical Details: The image quality assessment system implements convolutional neural networks (CNN)-based image quality assessment models (NIMA, BRISQUE) to achieve automatic judgment close to human visual quality assessment. Face image synthesis processing applies face super-resolution techniques (ESRGAN, Real-ESRGAN) and face enhancement algorithms to achieve high-quality restoration from low-resolution images. Thumbnail generation combines adaptive resizing, smart cropping, and content-aware scaling to perform efficient reduction while preserving important features. Image metadata management implements EXIF ​​data extraction, image fingerprinting (pHash, dHash), and duplicate detection to achieve efficient image management. Real-time image processing utilizes GPU acceleration (CUDA, OpenCL) and parallel processing to enable high-speed processing of large volumes of images. Privacy-preserving image processing applies differential privacy, k-anonymity, and face anonymization techniques to make personal identification difficult while maintaining surveillance capabilities. The web-based UI implements lazy loading, virtual scrolling, and progressive image loading, providing a comfortable user experience even when displaying a large number of thumbnails.

[0042] In at least one embodiment, the detection point visualization function using heat maps uses color intensity to represent the frequency of people and vehicles within a monitored area, helping to understand spatial patterns. This function overlays detection data on a facility floor plan or floor map, displaying color-coded data according to the frequency of occurrence. The color scale can be selected from a range of options, including gradual changes from cool to warm, transparency adjustments, and custom color palettes, optimizing the visual effect. Dynamic time-based display allows for animation of temporal patterns by time of day, day of the week, season, etc. The 3D heat map function provides a three-dimensional visualization, including vertical distribution. The system features a composite display function that overlays multiple data layers (people, vehicles, anomaly detections, etc.). Interactive functions allow users to click on specific areas to display detailed information, zoom and pan, and select ranges. Statistical overlays display numerical data such as the total number of detections, average stay time, and peak times on the map. The heat map data export function facilitates use in other GIS systems and analysis tools. By setting a threshold, areas with a density above a certain level are highlighted, indicating areas that require priority monitoring. Enhanced technical details: The heatmap generation algorithm combines Gaussian kernel density estimation, radial basis function interpolation, and inverse distance weighting to generate a smooth, visually understandable density distribution from sparse data points. Spatial statistical analysis applies spatial autocorrelation (Moran's I, Geary's C), hotspot analysis (Getis-Ord Gi*), and cluster analysis (LISA) to identify statistically significant clustering patterns. Real-time update functionality implements streaming data processing (Apache Kafka, Apache Storm) and incremental heatmap generation to instantly reflect newly detected data. 3D visualization utilizes WebGL technology (Three.js, Babylon.js) to enable high-performance browser-based 3D rendering. Large-scale data processing includes spatial indexing (R-tree, Quad-tree), data tiling, and level-of-detail rendering, enabling smooth visualization of data with millions of points. For color mapping, perceptually uniform color spaces (LAB, LUV) are used to achieve color changes that are suited to human visual characteristics.

[0043] In at least one embodiment, the person detection trend visualization function using a time series graph graphically displays temporal changes in detection data to support trend analysis and anomaly detection. This function plots indicators such as the number of detections, detection intervals, and dwell time along a time axis, enabling visual pattern recognition. Graph formats can be selected from line graphs, bar graphs, area graphs, scatter plots, etc., and the optimal representation is adopted depending on the nature of the data. Simultaneous display of multiple series enables comparative analysis by person, area, time period, etc. Time resolution can be set to minutes, hours, days, weeks, etc., allowing for adjustment of granularity according to the analytical purpose. Automatic detection and highlighting of outliers visually emphasizes deviations from normal patterns. Statistical analysis results such as trend lines, moving averages, and seasonal decomposition are overlaid on the graph. Interactive operation allows zooming in on specific periods, checking detailed data points, and displaying statistics by selecting a range. The forecast data display function visualizes future detection trends and supports planning preventative measures. Graphs can be output as images or PDFs, allowing them to be used in reports and meeting materials. Enhanced technical details: For time series data analysis, signal processing techniques (FFT, wavelet transform, empirical mode decomposition) are applied to separate and visualize periodicity, trends, and noise components. For anomaly detection algorithms, statistical process control (control charts, CUSUM), change point detection (PELT, Binary Segmentation), and outlier detection (STL decomposition + IQR) are combined. For interactive visualization, modern web technologies (D3.js, Observable Plot, Plotly.js) are utilized to provide a responsive, high-performance graphical interface. For large-scale time series data processing, data decimation, aggregation techniques, and rolling window computations are implemented to enable smooth visualization even with millions of data points. For statistical analysis functions, time series decomposition (STL, X-13ARIMA-SEATS), forecasting models (ARIMA, ETS, Prophet), and cross-correlation analysis are integrated to provide advanced time series analysis. For real-time updates, we implemented streaming visualization techniques, incremental chart updates, and efficient data binding to provide a continuous view of live data.

[0044] In at least one embodiment, the AI-based cluster classification and display function for similar faces and people with similar behavior utilizes machine learning technology to automatically identify highly related groups of people and support the detection of organized behavior. This function calculates similarity from multidimensional data such as facial feature vectors, behavioral patterns, and spatiotemporal correlations, and forms related groups using a clustering algorithm. The optimal clustering method is selected from k-means, hierarchical clustering, DBSCAN, spectral clustering, and other methods to achieve flexible classification according to the nature of the data. Similarity calculations comprehensively evaluate facial feature similarity, behavioral pattern similarity, correlation of appearance timing, and movement path similarity, among other factors. Cluster visualization represents relationships in formats such as dendrograms, scatter plots, and network graphs to support intuitive understanding. The dynamic clustering function automatically updates, splits, and merges clusters based on new detection data. It automatically identifies central and influential individuals within a cluster to support the determination of monitoring priorities. Detecting abnormal clusters (e.g., rapid expansion in a short period of time, abnormal behavioral patterns) enables early detection of organizational threats. By analyzing the relationships between clusters, we can infer the existence of larger organizational structures and superordinate groups. Enhanced technical details: For facial feature extraction, deep metric learning (ArcFace, CosFace, SphereFace) and triplet loss are used to achieve precise similarity calculation in high-dimensional feature spaces. For behavioral pattern analysis, recurrent neural networks (LSTM, GRU) are combined with attention mechanisms to learn complex patterns in time-series behavior data. For clustering optimization, ensemble clustering, consensus clustering, and multi-view clustering are applied to achieve robust classification that overcomes the limitations of single methods. Relationship learning using graph neural networks (GNN) quantifies complex interactions and influence relationships between people. Dimensionality reduction techniques (t-SNE, UMAP, PCA) are used to achieve low-dimensional visualization of high-dimensional feature spaces and improve interpretability. For dynamic network analysis, temporal network analysis, community evolution tracking, and influence propagation modeling are applied to track time-varying group structures. Explainable AI technologies (SHAP, LIME, attention visualization) visualize the characteristics that form the basis for cluster formation, improving the interpretability of analysis results.

[0045] In at least one embodiment, the listing function for specific clothing and movement patterns classifies and extracts targets based on appearance and behavioral characteristics, enhancing surveillance of specific threat types. This function comprehensively analyzes visual characteristics such as clothing color, pattern, shape, and brand logos, as well as behavioral characteristics such as walking speed, direction of movement, frequency of stops, and turning around. Clothing recognition utilizes a combination of image processing technologies, including fashion item detection, color analysis, texture analysis, and shape recognition. Movement pattern analysis quantifies individual behavioral characteristics using techniques such as posture estimation, gait analysis, and behavior classification. The automatic search function for specific conditions extracts targets based on complex criteria such as "wearing a black hat and mask," "walking unnaturally slowly," and "frequent turning around." Temporal patterns are considered to detect contextual anomalies such as "wearing all black at night" or "wearing clothing other than a suit during the day on a weekday." The similar pattern learning function automatically learns risky clothing and behavior patterns from past cases, improving detection accuracy. The pattern matching confidence score display also indicates the possibility of false positives, supporting a final human decision. Custom pattern definition allows you to define and add threat patterns specific to your facility. Enhanced Technical Details: The clothing recognition system applies the latest computer vision techniques (YOLO, Faster R-CNN, and Mask R-CNN) to achieve high-precision object detection and segmentation in real time. Color analysis combines quantitative color evaluation in HSV color space and LAB color space with color histogram matching to achieve robust color recognition against lighting changes. Texture analysis utilizes Gabor filters, Local Binary Patterns (LBP), and Gray-Level Co-occurrence Matrix (GLCM) to extract detailed features of clothing materials and patterns. Pose estimation uses human pose estimation libraries such as OpenPose, MediaPipe, and AlphaPose to accurately detect joint positions and analyze motion. Gait analysis utilizes gait recognition algorithms, spatiotemporal gait parameters, and frequency domain analysis to quantify individual gait characteristics. For abnormal behavior detection using machine learning, we utilize one-class SVM, autoencoders, and generative adversarial networks (GANs) to automatically detect deviations from normal behavior. For behavior prediction, we apply sequence-to-sequence models and transformer architectures to predict future behavior from current behavior patterns.

[0046] In at least one embodiment, a list display function with correlation analysis between visit intervals and stay times enables multifaceted evaluation of abnormalities through detailed analysis of temporal behavior patterns. This function statistically analyzes the relationship between each individual's visit interval (time elapsed since the last visit) and stay time (time spent per visit) and visualizes it using scatter plots, regression lines, correlation coefficients, etc. Normal ranges are established using statistical distributions from past data, comparisons with business patterns, and adjustments based on time of day and day of the week. Abnormal patterns are detected by identifying characteristic combinations such as "short intervals with long stays," "long intervals with short stays," and "irregular interval fluctuations." Individual behavioral profiling learns each individual's unique normal patterns and enables personalized anomaly detection. Correlation changes over time are tracked to detect changes in behavior patterns and the progression of abnormalities. A correction function that takes crowd psychology and social factors into account adjusts for the effects of external factors such as events, weather changes, and social conditions. By linking with predictive models, future visit times and stay times are estimated, supporting the optimization of security plans. Statistical significance testing of correlation analysis results is used to distinguish between coincidence and necessity. Correlation analysis across multiple time scales (days, weeks, months) is used to evaluate behavioral patterns from both short-term and long-term perspectives. Enhanced technical details: The correlation analysis system implements advanced statistical methods (Pearson correlation, Spearman correlation, Kendall's tau, partial correlation) to quantify various relationship patterns. Dynamic correlation analysis, rolling correlation, and wavelet coherence analysis are applied to time series analysis to perform detailed analysis of time-varying correlations. The anomaly detection algorithm combines multivariate anomaly detection (Mahalanobis distance, One-Class SVM, Isolation Forest) to detect abnormalities in complex behavioral patterns. Statistical significance testing implements bootstrap methods, permutation tests, and Bayesian hypothesis testing to achieve robust statistical inference. Machine learning behavior prediction utilizes time series forecasting models (ARIMA, LSTM, Prophet) and regression models (Random Forest, XGBoost) to provide highly accurate future forecasts. The visualization feature implements interactive statistical plots (scatter plots with regression lines, correlation heatmaps, and time series correlation plots) to support intuitive pattern understanding. Covariate analysis, confounding factor adjustment, and causal inference methods are applied to analyze the influence of external factors, enabling separation of true behavioral patterns from environmental factors.

[0047] In at least one embodiment, the function for listing suspected stalking behavior automatically detects behavioral patterns characteristic of stalking behavior and helps prevent victimization. This function comprehensively analyzes behavioral elements typical of stalking behavior, such as frequent appearances, following a specific individual, lying in wait, and hidden observation. To learn behavioral patterns, a comprehensive model is constructed based on known stalking cases, expert knowledge, legal definitions, etc. A scoring system that combines multiple behavioral indicators (such as appearance frequency, length of stay, movement patterns, and gaze behavior) enables quantitative assessment of stalking likelihood. Relationship analysis between the target and potential victim detects obsessive behavior toward a specific individual. Temporal pattern analysis detects synchronization with the victim's activity schedule and predictive preemptive behavior. Spatial pattern analysis identifies lying in wait at the victim's activity location and appearances along the victim's movement route. Detection of escalation provides early detection of escalation of behavior and increased danger. A privacy protection function ensures confidentiality of victim information and ensures that only appropriate authorized personnel can access it. The system supports rapid reporting and coordination with law enforcement agencies in the event of a serious threat, and recommends specific measures such as strengthened security, evacuation guidance, and legal action through its damage prevention proposal function. Enhanced technical details: For stalker behavior detection, behavioral analytics and pattern recognition are combined to enable real-time detection of complex behavioral patterns using complex event processing (CEP). For machine learning models, ensemble methods (Random Forest, Gradient Boosting, Neural Networks) are utilized to achieve highly accurate stalker likelihood determination from multidimensional behavioral data. For spatiotemporal correlation analysis, spatiotemporal data mining, trajectory analysis, and co-location mining are applied to quantify behavioral synchronization between the target and victim. For behavioral prediction models, Markov models, hidden Markov models, and recurrent neural networks are used to predict the escalation of stalker behavior. Privacy protection technologies include differential privacy, homomorphic encryption, and secure multi-party computation to perform analysis while maintaining confidentiality. For legal evidence collection functions, digital forensics standards (ISO 27037), chain of custody management, and tamper-evident logging are implemented to ensure evidence preservation in accordance with legal procedures. The risk assessment function integrates threat assessment frameworks, violence prediction models, and risk stratification algorithms to provide an objective assessment of risk.

[0048] In at least one embodiment, the automatic generation function for a daily high-frequency visitor list automatically extracts individuals with an abnormally high number of visits per day and displays them as priority targets for monitoring that day. This function accommodates daily fluctuations in the normal range by setting adaptive thresholds that take into account business hours, work patterns, seasonal factors, etc. The list generation time can be set to update the list at multiple times, such as the start of work, lunch break, and end of work. The visit count method can be selected from simple entry counts, visits with stays, and stays with significant activity, optimizing it according to the monitoring purpose. Visits with an abnormal frequency for that individual are detected by comparison with individual visit history. A whitelist function is provided to exclude legitimate high-frequency visitors such as business associates, delivery personnel, and maintenance workers. A real-time update function dynamically updates the list according to changes in visit status during the day. A function to estimate the cause of high-frequency visits classifies and displays possible business purposes, personal reasons, abnormal behavior, etc. Comparison with past data distinguishes between continuous high-frequency visitors and temporary high-frequency visitors. The automatic notification function instantly notifies relevant parties of the appearance of frequent visitors exceeding the set threshold. Enhanced technical details: The dynamic threshold setting system combines adaptive thresholding algorithms (CUSUM, EWMA control charts), seasonal decomposition, and trend analysis to automatically adjust time-varying normal ranges. Machine learning-based visitor purpose classification integrates natural language processing (NLP), computer vision, and behavioral analysis to automatically infer purposes from visitor behavior. Apache Kafka, Apache Flink, and Redis Streams are used for real-time streaming processing, enabling instant processing and updating of large volumes of visitor data. Statistical anomaly detection uses time series anomaly detection (STL+IQR, Prophet anomaly detection, Isolation Forest) to detect anomalous visitor patterns at the individual level. Whitelist management combines machine learning-based automatic classification (business visitor classification, delivery personnel recognition) with a manual override function to efficiently manage legitimate visitors. Predictive analytics capabilities include demand forecasting models, visitor flow prediction, and capacity planning algorithms to provide forecasts of future visitor patterns and required responses.

[0049] In at least one embodiment, the visitor behavior deviation prediction function using an anomaly detection model utilizes machine learning technology to predict deviations from normal visitor patterns in advance, enabling preventative measures. This function learns normal baseline patterns from past visitor data and detects signs of anomalies by combining techniques such as statistical anomaly detection, time series forecasting, and pattern matching. The predictive model uses advanced algorithms such as ARIMA, LSTM, transformer, and isolation forest to capture complex temporal dependencies and nonlinear patterns. Multi-layered anomaly detection at the individual, group, and overall levels detects deviations at various scales. External factors (weather, events, social conditions, etc.) are incorporated as explanatory variables to consider normal behavioral changes due to environmental changes. Confidence intervals for predictions are displayed to quantify uncertainty and provide reference information for decision-making. Ensemble learning integrates predictions from multiple models to achieve more robust anomaly detection. Online learning functions continuously update models based on new data. Prediction results are visualized in a variety of formats, including time series graphs, probability distributions, and risk maps. The model's interpretability function clarifies the factors that underlie anomaly predictions and assists in the development of countermeasures. Enhanced technical details: For anomaly detection architecture, we built deep learning frameworks (TensorFlow, PyTorch, Keras) and an MLOps pipeline to automate model training, validation, and deployment. For time series anomaly detection, we utilized variational autoencoders (VAE), generative adversarial networks (GANs), and transformer-based models (Informer, Autoformer) to learn complex temporal patterns and detect anomalies. For feature engineering, we implemented automated feature selection, dimensionality reduction (PCA, t-SNE, UMAP), and feature importance analysis to automatically select the optimal feature set. For model interpretability, we integrated explainable AI techniques (SHAP, LIME, attention visualization, integrated gradients) to visualize the basis for prediction results. For uncertainty quantification, we implemented Bayesian neural networks, Monte Carlo dropout, and ensemble uncertainty to provide a reliability assessment of predictions. Implement A / B testing, model monitoring, and performance degradation detection to continuously evaluate and improve model performance in production.

[0050] In at least one embodiment, an AI-based automatic suspicious behavior scoring function comprehensively evaluates various behavioral indicators and quantifies the threat level of each target. This function uses machine learning to integrate multiple factors, such as visit frequency, length of stay, movement patterns, gaze behavior, and contact attempts, and expresses the risk level with a score from 0 to 100. Score calculation uses methods such as weighted sum, neural network, decision tree, and ensemble method to adaptively adjust the importance of each factor. Characteristics of behavioral factors are extracted using multidimensional information such as time series patterns, spatial distribution, social context, and personal history. Dynamic score updating recalculates scores in real time based on new behavioral data, allowing for rapid response to changing situations. A score distribution normalization function appropriately maintains the overall threat level distribution and ensures the stability of relative assessments. Threshold-based automatic classification automatically categorizes targets into categories such as low risk, medium risk, high risk, and maximum alert. Detailed display of score components clearly identifies behavioral factors that cause high scores and clarifies the focus of countermeasures. By comparing with past scores, the system tracks trends in threat levels and enables early detection of escalations. Explainable AI technology makes the basis for score calculations visible, improving the transparency and reliability of the system. In addition, a multi-layered scoring system enables hierarchical evaluation at the individual, group, time period, and area levels. The machine learning model combines deep neural networks, convolutional neural networks, and recurrent neural networks to learn complex nonlinear patterns. Transfer learning technology utilizes models that have been trained in similar environments to achieve highly accurate scoring even with limited data. The online learning function continuously updates the model with new data obtained during operation, improving adaptability to environmental changes. When combined with an anomaly detection algorithm, it can automatically detect new and previously unseen threat patterns.

[0051] In at least one embodiment, the pattern anomaly detection function based on past data matching searches for similar patterns in a database accumulated over a long period of time and performs comparative analysis with current behavior. This function evaluates the similarity between past anomaly cases and current situations using techniques such as time series pattern matching, similarity calculation, and statistical testing. Pattern comparison uses time series analysis techniques such as DTW (Dynamic Time Warping), cross-correlation, and mutual information to achieve flexible matching that takes time lags and fluctuations into account. Multidimensional pattern comparison identifies complex anomaly patterns that are difficult to detect using a single indicator. Threat types are estimated based on the categorization of past cases (e.g., intrusion, theft, stalking, vandalism, etc.). Similarity score thresholds are set to extract only past cases with significant similarity and eliminate noise. A time-based weighting function assigns higher importance to recent cases. Geographical considerations prioritize past cases in the same region or similar environments. Pattern evolution tracking detects changes in criminal methods and the emergence of new threat patterns. Visualization of detection results helps users understand abnormalities by overlaying similar patterns and highlighting differences. In addition, time series decomposition technology separates trend components, seasonal components, and random components, enabling anomaly detection at each component level. Frequency analysis such as Fourier transform and wavelet transform strengthens the identification of periodic abnormal patterns and sudden anomalies. Markov chain Monte Carlo method enables the detection of abnormal patterns with complex probability distributions. Combining it with clustering technology enables the grouping of similar abnormal patterns and the automatic discovery of new anomalies. A meta-learning approach efficiently learns abnormal patterns from a small number of samples, improving the ability to respond to rare cases. Federated learning technology enables distributed anomaly detection that protects privacy while integrating data from multiple locations.

[0052] In at least one embodiment, a cross-border surveillance function that links with cameras at other locations creates a wide-area surveillance network spanning multiple facilities and regions, enabling the detection of organizational threats and widespread movement. This function integrates the management of camera systems at multiple locations and automatically correlates the appearance of the same individual at different locations. Data sharing between locations uses secure communication methods such as VPNs, dedicated lines, and cloud services to ensure data confidentiality and integrity. A multiplexing system that combines facial recognition, vehicle identification, and behavioral pattern analysis is built to accurately identify the same individual. An automatic time zone adjustment function maintains accurate time synchronization between locations in different time zones. Validation of travel time eliminates physically impossible short-distance travel and prevents false matches. Analysis of movement patterns between locations supports the identification of planned organizational activities and escape routes. A wide-area alert function instantly shares detections at one location with other locations, enabling coordinated responses. A privacy protection function limits the information shared between locations to the minimum necessary, ensuring appropriate management of personal information. By setting permissions for each location, information viewing and editing permissions can be appropriately controlled to ensure security. Furthermore, the distributed database architecture enables necessary information sharing while maintaining the autonomy of each base. The data synchronization protocol uses event sourcing and the CQRS (Command Query Responsibility Segregation) pattern to ensure high consistency and availability. Integration with a geographic information system (GIS) visually represents the geographic relationships and movement routes between bases. Satellite communication and mesh network technology maintain cooperation between bases even during disasters or communication failures. A distributed ledger using blockchain technology prevents data tampering between bases and ensures an audit trail. Predictive modeling using artificial intelligence predicts the next appearance base based on past movement patterns, supporting preemptive security deployment.

[0053] In at least one embodiment, the revisit prediction function based on person attribute learning learns the relationship between behavioral patterns and external characteristics such as age, gender, clothing, and body type, and predicts the likelihood of future visits. This function integrates attribute extraction through image analysis, correlation analysis with behavioral data, and predictive model construction. Attribute recognition uses computer vision technologies such as facial age estimation, gender determination, clothing classification, and body type estimation to automatically extract various personal characteristics. Correlation analysis between attributes and behavior discovers statistical relationships such as "the pattern of middle-aged men visiting on weekday evenings" and "the tendency of young women to stay in the afternoon on weekends." A time series prediction model estimates future visit timing, frequency, and duration based on individual attributes. Group-level prediction predicts the behavior of new visitors based on the behavioral trends of groups of people with similar attributes. High-precision predictions are achieved by taking into account the interactions between attributes and time factors such as seasonality, day-of-the-week effects, and time-of-day effects. Quantifying the uncertainty of the prediction provides prediction results in confidence intervals and probability distributions. The attribute change tracking function automatically updates the prediction model based on changes in clothing, age, body shape, etc. By utilizing the prediction results, it supports operational improvements such as optimizing personnel deployment and streamlining security plans. In addition, latent representation learning through deep learning automatically discovers subtle attribute patterns that are difficult for humans to recognize. Multimodal learning achieves comprehensive attribute modeling that integrates visual information, audio information, and behavioral history. Continual learning technology prevents catastrophic forgetting when learning new attribute patterns. A meta-learning approach efficiently learns individual-specific attribute patterns from a small number of samples. Explainable AI technology visualizes the basis for prediction decisions, ensuring system transparency. As a privacy protection function, differential privacy technology extracts only statistical information in a form that does not identify individuals. Federated learning achieves distributed protection of personal information while integrating data from multiple locations.

[0054] In at least one embodiment, the predictive visitor detection function detects pre-visit behaviors prior to an actual facility visit, allowing for time to prepare. This function automatically identifies predictive behaviors such as loitering around the perimeter, scouting, driving the wrong way, and unnatural stops, and assesses the possibility of intrusion or criminal activity in advance. A comprehensive model is built to define predictive behaviors, integrating expert knowledge, analysis of past cases, and behavioral psychology theory. Spatial predictive behavior detection monitors abnormal activity around the facility, observational behavior near the boundary, and investigation of intrusion routes. Temporal predictive behavior detection identifies appearances at unusual times of day, gradual approach patterns, and repeated attempts. Behavioral predictive behavior detection detects psychological and physical changes such as unnatural behaviors, signs of tension, and concealing behaviors. By integrating and evaluating multiple predictive behaviors, even minor behaviors can be combined to identify them as signs of a serious threat. By predicting the time from the predictive behavior to its execution, a timeline for implementing countermeasures can be established and effective preventive measures can be planned. A tiered warning system promotes appropriate responses according to the level of the warning sign. The learning function of the warning sign detection enables continuous improvement by adapting to new patterns and changes in techniques. Furthermore, a behavioral prediction model based on environmental psychology will take into account the impact of the physical environment on human behavior. Gaze pattern analysis, which mimics eye-tracking technology, will estimate a subject's interests and intentions. Linking with social media analysis will detect online advance notices and preparatory behavior. Correlation analysis with weather data will improve prediction accuracy by taking into account the impact of weather on predictive behavior. Knowledge of behavioral economics will be utilized to build a predictive model of predictive behavior that takes into account the human decision-making process. Information from multiple sensors (acoustics, vibrations, electromagnetic waves, etc.) will be integrated to detect predictive changes that cannot be captured by visual information alone. Spatiotemporal data mining technology will automatically discover predictive patterns from large-scale spatiotemporal data.

[0055] In at least one embodiment, the automatic update function for the list of suspected individuals based on appearance frequency dynamically adjusts the priority of individuals requiring vigilance based on continuous monitoring data, enabling efficient allocation of monitoring resources. This function comprehensively evaluates factors such as the rate of change in appearance frequency, cumulative risk score, and recent behavioral patterns to automatically promote or demote suspected individuals to a higher or lower level. The update algorithm uses statistical methods such as exponential smoothing, Kalman filtering, and Bayesian updating to optimize the balance between past data and the latest information. Individual learning periods are set to enable adaptive evaluation, such as short-term intensive monitoring of newly detected individuals and focusing on long-term trends for continuously monitored individuals. Dynamic adjustment of list capacity optimizes the number of suspected individuals based on the overall threat level distribution. The automatic exclusion function gradually removes individuals who have not exhibited abnormal behavior for a long period of time from the list, improving monitoring efficiency. The manual intervention function allows administrators to make emergency registrations or exclusions, compensating for the limitations of automated systems. Recording of update history supports tracking and auditing of decision-making rationales. A list change notification function automatically reports important priority changes to relevant parties. Predictive updates enable adjustments of monitored individuals to anticipate future risk changes. In addition, a multi-objective optimization algorithm is used to simultaneously optimize multiple goals such as monitoring efficiency, detection accuracy, and resource constraints. Reinforcement learning technology automatically learns the optimal update strategy from past update decisions and their results. Graph neural networks enable list updates that take into account the relationships between subjects. Time series clustering groups subjects with similar appearance patterns to formulate efficient monitoring plans. Outlier detection technology enables early detection of new threats that show statistically abnormal appearance patterns. Multi-agent systems enable multiple monitoring agents to work together to achieve optimal list management. Digital twin technology enables prior evaluation of update strategies through simulation in a virtual environment.

[0056] In at least one embodiment, a risk scoring function linked to local crime history integrates local crime statistics and surveillance data to perform threat assessments that take geographic and temporal context into account. This function correlates and analyzes the facility's own surveillance data with external data such as police statistics, local safety information, and incident histories at nearby facilities. Geographic mapping of crime data identifies high-risk areas, crime-prone times, and trends in crime methods, and reflects these in the behavioral assessment of monitored targets. Time-series crime trend analysis enables dynamic risk assessments that take into account seasonality, day-of-the-week effects, and correlations with social events. A crime type weighting function adjusts the threat level according to the type of crime, such as theft, break-ins, and violent crime. Spatial proximity consideration adjusts the influence of crime history according to distance from the facility. Temporal proximity consideration applies higher weighting to recent incidents to appropriately reflect the current threat level. Crime technique matching increases the vigilance level when monitored targets' behavioral patterns are similar to known criminal techniques. A regional cooperation function establishes a wide-area crime prevention system in cooperation with neighboring facilities, sharing crime information. By taking into consideration legal constraints, we will maintain an appropriate balance between protecting personal information and public safety. Furthermore, a crime prediction model based on machine learning is used to estimate the probability of future crime occurrences based on past crime patterns. Advanced integration with geographic information systems (GIS) enables precise risk analysis that takes into account geographic factors such as topography, building layout, and transportation networks. Social network analysis is used to evaluate the relationships between criminals and the possibility of organized crime. Spatiotemporal clustering technology is used to automatically classify crime occurrence patterns and support the early detection of similar patterns. Causal inference techniques are used to identify the causal relationship between environmental factors and crime occurrences, and to propose effective crime prevention measures. Anomaly detection algorithms are used to automatically detect new threats that deviate from past crime patterns. A dynamic risk assessment system is built that takes into account changes in crime trends during disasters.

[0057] In at least one embodiment, a future intrusion prediction map generation function based on crime prevention history analyzes past incident data and environmental factors to visualize the spatial distribution of future intrusion risk. This function learns intrusion patterns from data such as past intrusion locations, times, methods, and damage situations, and predicts future risk distribution using machine learning. The prediction model incorporates geographical factors (location, surrounding environment, accessibility), temporal factors (season, day of the week, time of day), and social factors (people flow, events, security situation), etc., as explanatory variables. Spatial statistical methods are used to perform spatial clustering of intrusion incidents, identify hotspots, and predict diffusion patterns. Temporal prediction supports both short-term predictions (days to weeks) and long-term predictions (several months to a year), enabling use according to the timeframe of security planning. The environmental change reflection function incorporates changing factors such as building renovations, increased security, and surrounding development into the prediction model. Uncertainty visualization expresses the reliability of predictions using color intensity and contour lines, providing reference information for decision-making. The effectiveness of countermeasures can be evaluated in advance using a simulation function. Regular model updates ensure continuous improvement in prediction accuracy based on new incident data. In addition, image analysis using deep learning automatically assesses intrusion risk based on visual features such as the building's exterior, surrounding environment, and security equipment. Graph neural networks predict the propagation of intrusion risk, taking into account relationships between buildings and regional networks. Generative models (GAN, VAE, etc.) automatically generate intrusion scenarios under various conditions, improving the comprehensiveness of countermeasures. Multi-agent simulation dynamically models the interaction between intruder behavior strategies and security measures. Optimization algorithms calculate optimal deployment strategies with limited security resources. Integration with real-time data enables dynamic updates of predictive maps based on the current situation. Virtual reality technology provides immersive risk visualization in 3D space.

[0058] In at least one embodiment, the subject record evidence function creates and manages data with high evidentiary value for legal proceedings. This function manages records to meet forensic requirements, such as ensuring the integrity of detected data, ensuring the legal validity of timestamps, and maintaining a chain of custody. To prevent data tampering, hash value calculations, electronic signatures, blockchain technology, and other technologies are used to ensure the reliability of data as evidence. Detailed metadata recording comprehensively preserves technical information such as imaging conditions, processing history, analysis methods, and decision-making grounds. The automatic report generation function conforming to legal formats enables output in purpose-specific formats, such as for police submissions and court documents. Consideration for personal information protection is given by extracting only the information necessary for legal proceedings and automatically excluding irrelevant personal information. The automatic evidence preservation alert function provides advance notification of expiration of retention periods, detection of data corruption, and other events. Detailed access log recording enables tracking of all access history to evidentiary data. The multiple format output function enables provision in formats such as PDF, XML, and standardized electronic evidence formats. The expert testimony support function automatically generates explanatory materials and statistical data on technical grounds. In addition, forensic image analysis technology automatically verifies the authenticity of images, detects tampering, and evaluates their quality. Digital forensic technology enables the recovery of deleted data and the extraction of hidden information. Cryptographic hash chains mathematically guarantee the chronological integrity of data. Integration with public key infrastructures (PKI) creates a strong certificate-based authentication system. Automatic generation of audit trails records all data processing processes in detail, enabling later verification. Data handling procedures are automated in accordance with international standards (such as ISO 27037), maximizing the evidentiary value in legal proceedings. A cloud-based evidence preservation system enables secure, geographically distributed storage and disaster prevention. Artificial intelligence-assisted evidence analysis efficiently extracts legally important evidence from large amounts of data.

[0059] In at least one embodiment, the automatic blacklisting function for frequent repeat visitors automatically adds subjects exhibiting abnormal behavioral patterns to a watch list, establishing an enhanced monitoring system. This function allows for multi-level setting of registration criteria, enabling automatic classification into levels such as caution, caution, danger, and maximum caution. Registration decisions are made using composite indicators such as frequency of occurrence, time patterns, behavioral characteristics, and past history, providing a comprehensive evaluation beyond simple frequency alone. The automatic registration notification function immediately reports new blacklist registrations to administrators and provides an opportunity for manual review. The automatic deregistration decision allows for a gradual lowering of the watch level for subjects exhibiting improved behavioral patterns. Hierarchical blacklist management controls the gradual sharing of information, including internal lists, law enforcement shared lists, and industry-wide lists. Detailed recording of the reasons for registration allows for tracking of the basis for the decision and subsequent verification. Automatic validity period management enables automatic review after a certain period and expiration notification. The legal consideration function appropriately maintains the balance between human rights protection and public safety and prevents unjustified surveillance. The external database matching function automatically checks for matches with known suspect individuals. In addition, detailed analysis of behavioral patterns automatically identifies specific threat types such as stalking, reconnaissance, and organized reconnaissance activities. Machine learning anomaly detection accurately detects deviations from an individual's unique normal pattern. Time series anomaly detection technology distinguishes and evaluates gradually changing behavioral patterns and sudden anomalies. Social network analysis evaluates the relationship between registered individuals and known dangerous individuals. Sentiment analysis technology estimates the individual's psychological state and intentions, improving the accuracy of threat levels. Privacy protection technology achieves both appropriate anonymization processing and compliance with legal requirements. Multi-criteria analysis of risk assessment enables objective registration decisions that integrate multiple criteria. An automatic notification system automatically shares information in stages with relevant departments, security companies, and law enforcement agencies.

[0060] In at least one embodiment, the same-subject tracking analysis function integrates multiple cameras to enable comprehensive behavioral monitoring throughout the facility and record detailed movement trajectories. This function automatically performs target handover in overlapping camera views, estimates the target's position in blind spots, and reconstructs the entire movement pattern. A robust matching system is built to uniquely identify targets by combining multiple techniques, including facial recognition, gait characteristics, clothing patterns, and body characteristics. Taking into account spatiotemporal constraints, the system detects physically impossible movements and time series inconsistencies and automatically corrects tracking errors. Three-dimensional movement trajectory reconstruction accurately records three-dimensional behavioral patterns, such as movement between floors, elevator use, and staircase movement. Behavioral analysis extracts detailed behavioral characteristics, such as movement speed, stopping points, dwell time, and objects of interest. Detection of abnormal movement patterns automatically identifies unusual movements, such as getting lost, wandering, intrusions, and escapes. A prediction function predicts the next destination and behavior from current movement patterns, supporting proactive security deployment. Privacy protection features allow access control to detailed tracking data and anonymization as needed. Statistical analysis provides operational improvement data for facility usage patterns, traffic flow optimization, security efficiency, etc. In addition, the multi-object tracking (MOT) algorithm enables simultaneous tracking of multiple people and maintains personal identification. Deep learning-based re-identification technology enables robust person matching despite changes in clothing, lighting, and viewpoint. State estimation technologies such as Kalman filters and particle filters ensure tracking continuity even in the event of temporary occlusion or detection failure. Graph neural networks enable high-precision tracking by integrating the spatial relationships between cameras and people's movement history. Edge computing technology performs local tracking processing on each camera, streamlining integrated management on a central server. Digital twin technology enables tracking visualization in a 3D virtual space and bidirectional linkage with the physical space. Optimization of the time series database enables high-speed search and long-term storage of large amounts of tracking data.

[0061] In at least one embodiment, an information sharing function with security companies and local governments via API integration enables rapid information transmission and collaborative response between organizations, strengthening crime prevention throughout the region. This function uses standard protocols such as RESTful API, SOAP, and GraphQL to enable smooth data exchange between different systems. Security features include OAuth authentication, API key management, SSL / TLS encryption, and rate limiting to prevent unauthorized access and information leaks. Data format standardization allows information to be provided in common formats such as JSON, XML, and CSV, simplifying processing on the receiving system. Real-time notification supports immediate information sharing and collaborative response in the event of an emergency. Permission-based access control appropriately manages information disclosure levels by organization and individual. Data anonymization ensures the sharing of only necessary security information while protecting privacy. Logging maintains audit trails, enabling transparency of information access and accountability. Automatic switching in the event of a failure ensures information is transmitted via an alternative route in the event of a communication failure or system downtime. The two-way communication function not only allows for the provision of information, but also for receiving instructions from security companies and issuing alerts from local governments. In addition, the microservice architecture implements each function as an independent service, improving the flexibility and scalability of the system. The event-driven architecture enables real-time notification of state changes and asynchronous processing. The API Gateway pattern provides multiple backend services with a unified interface. The circuit breaker pattern prevents failures in external systems from spreading to the entire system. Distributed tracing technology visualizes the processing flow between complex microservices and helps to quickly identify problems. Container technology (Docker, Kubernetes) improves system portability and operational efficiency. GraphQL Subscription enables efficient delivery of real-time data. The webhook function enables automatic notification of external systems when events occur.

[0062] In at least one embodiment, a psychological state analysis function based on facial expression and emotion recognition estimates a subject's psychological state from subtle changes in facial expressions and detects potential threats or signs of abnormal behavior. This function uses deep learning-based facial expression recognition technology to automatically classify basic emotions such as joy, anger, sadness, fear, surprise, disgust, and neutral. Microexpression analysis detects true psychological states from intentionally concealed emotions and momentary changes in facial expressions. Tracking patterns of emotional changes over time detects the progression of dangerous psychological states such as increased tension, growing hostility, and panic. Stress indicator calculation quantifies psychological stress from stiffened facial expressions, unnatural smiles, and eye movements. Abnormal psychological pattern detection identifies tension before a crime, anxiety during an escape, and changes in facial expression when lying. A learning function that takes individual differences into account sets each person's normal facial expression as a baseline to enable personalized anomaly detection. A cultural background consideration function incorporates differences in facial expressions due to ethnicity and region into the learning process. To ensure privacy, we will implement anonymization of emotion data and appropriate storage period management. By integrating it with psychological knowledge, we will improve the accuracy of behavior prediction and propose appropriate countermeasures. Furthermore, by integrating the latest computer vision and affective computing technologies, the system detects detailed changes in facial expressions through 3D facial shape analysis. Combining it with an infrared camera enables facial expression recognition in dark places and stress detection based on changes in body temperature. By integrating it with voice emotion analysis, it achieves a comprehensive emotional assessment of facial expressions and voice. Correlation analysis with biosignals (heart rate, breathing, etc.) estimates internal psychological states that are difficult to detect through facial expressions alone. Knowledge of group psychology is utilized to detect the spread of emotions in crowds and collective agitation. Machine learning explainability technology visualizes the basis for emotion determination, ensuring system transparency. Optimizing real-time processing enables simultaneous emotion analysis of multiple people with low latency. Edge AI technology enables instant emotion analysis on the spot while protecting privacy.

[0063] In at least one embodiment, the voice recognition and conversation anomaly detection function analyzes audio within the monitored area to automatically detect threats, violent remarks, abnormal conversation patterns, etc. This function combines acoustic processing technologies such as high-performance microphone array sound collection, noise cancellation, and sound source separation. Voice recognition uses deep learning-based automatic speech recognition technology to accommodate a variety of voice patterns, including multiple languages, dialects, whispers, and rapid speech. Natural language processing of the conversation content performs sentiment analysis, intent estimation, and threat level determination. Keyword detection instantly detects the appearance of dangerous words such as "bomb," "kill," and "threat" and triggers an alert. Voice pattern analysis detects emotional changes such as shouts, screams, and abnormal excitement. Speaker identification tracks individual speech in multi-person conversations and compares it with past voice data. Environmental sound analysis also detects physical anomalies such as shattering glass, metallic sounds, and unusual mechanical sounds. The privacy protection feature ensures appropriate anonymization of conversation content and record keeping in accordance with legal requirements, while the real-time translation feature instantly understands conversations in foreign languages, making it suitable for international surveillance environments. In addition, acoustic feature extraction estimates a speaker's physical and psychological state from voice tremors, shortness of breath, changes in voice quality, etc. Voice synthesis detection technology detects deception using deepfake voices and voice conversion technology. Multi-speaker simultaneous recognition technology enables the separation and understanding of individual voices in complex acoustic environments where multiple people are speaking simultaneously. Knowledge of emotional acoustics is utilized to estimate emotional states not only from linguistic content but also from acoustic features. Acoustic event detection automatically identifies important acoustic information other than human voices (gunshots, explosions, intrusion sounds, etc.). Integration with smart speaker technology enables the operation of surveillance systems through natural voice dialogue. 3D acoustic analysis enables sound source location estimation and movement tracking. The application of quantum machine learning enables the recognition of complex acoustic patterns that are difficult to achieve with conventional methods. Collaboration with brain-computer interface technology promotes research and development into reading intentions and emotions from electroencephalogram signals other than speech.

[0064] In at least one embodiment, the drone-linked automatic tracking function integrates unmanned aerial vehicles and ground camera systems to achieve comprehensive surveillance and dynamic target tracking in three-dimensional space. This function automatically transfers a target detected by a fixed camera to a drone, enabling continuous tracking from the air. The drone's automatic launch system enables rapid deployment when an emergency is detected and automatic flight according to a pre-defined flight plan. High-precision automatic flight combining GPS guidance and computer vision enables safe tracking flight in complex environments, such as between buildings and trees. Cooperative control of multiple drones enables simultaneous wide-area surveillance, multi-angle photography, and long-term relay tracking. Real-time video transmission enables high-quality video distribution to a ground monitoring center and automatic analysis using AI. The battery management function supports unmanned operations, including automatic charging, battery replacement, and automatic switching to a backup aircraft. The weather response function makes safe flight decisions taking into account environmental factors such as wind speed, precipitation, and visibility. The aviation regulatory compliance function enables avoidance of no-fly zones, compliance with altitude restrictions, and automatic acquisition of flight permits. Furthermore, autonomous flight control using artificial intelligence allows for complex tracking missions without pilot intervention. SLAM (Simultaneous Localization and Mapping) technology allows for simultaneous self-localization and map creation in unknown environments. Swarm intelligence algorithms allow multiple drones to work together to form an efficient surveillance network. Edge computing technology allows the drone's onboard AI processor to perform instant image analysis and decision-making. Integration with a solar power generation system enables long flight times and reduced environmental impact. 5G communication technology enables low-latency transmission of large volumes of video data. Quantum cryptography communication ensures secure transmission of confidential video data from drones. Blockchain technology prevents tampering with flight records and collected data. Biometric authentication prevents unauthorized drone operation and ensures security.

[0065] In at least one embodiment, 3D surveillance functionality using AR / VR technology utilizes augmented reality and virtual reality technologies to provide a three-dimensional, intuitive surveillance environment that goes beyond traditional 2D images. This functionality integrates video from multiple cameras to generate a 3D model, enabling free-viewpoint surveillance within a virtual space. AR display overlays target information, movement trajectories, warning displays, and other information on real-world surveillance footage to support efficient situational awareness. VR environments enable immersive surveillance, allowing monitors to move freely within the virtual space and observe targets from any angle. 3D human modeling records and analyzes detailed physical characteristics of targets, such as height, body shape, and posture, in three dimensions. Visualization of spatial relationships allows intuitive understanding of physical relationships between targets, such as distance, relative position, and crowd formation. Time-series 3D playback allows past events to be reproduced in three dimensions, enabling detailed post-event analysis. Haptic feedback provides tactile notifications of abnormalities and the sensation of physical manipulation in the virtual space. The multi-user VR function allows multiple observers to share the same virtual space, enabling collaborative monitoring. In conjunction with a 360-degree camera, it enables three-dimensional display of omnidirectional images and comprehensive monitoring without blind spots. In addition, the fusion of augmented reality technology and artificial intelligence will visually display real-time threat prediction information and recommended response procedures. Spatial computing technology will realize a next-generation surveillance interface that seamlessly integrates physical space and virtual information. Gesture recognition technology will enable intuitive operation of VR space using hand and body movements. Eye tracking technology will automatically detect the observer's attention and interest, enabling efficient information presentation. Haptic feedback technology will provide a physical sensation when interacting with virtual objects. Brainwave interface technology will enable research and development of direct system operation through thought. Quantum computing technology will enable ultra-high speed processing of complex 3D calculations. Digital twin technology will create a complete virtual replica of a real surveillance environment to support simulation-based training and planning.

[0066] In at least one embodiment, the weather data integration function analyzes the correlation between weather conditions and human behavior to achieve highly accurate behavioral predictions that take weather factors into account. This function acquires detailed weather information, such as temperature, humidity, wind speed, precipitation, and sunshine, from data from the Japan Meteorological Agency, private weather services, and on-site sensors. Seasonal analysis learns the impact of seasonal changes (spring, summer, fall, and winter) on human behavior patterns and incorporates this information into the prediction model. Analysis of behavioral patterns by weather identifies differences in visit frequency, length of stay, and movement patterns under weather conditions such as sunny, rainy, snowy, and strong winds. Integration with weather forecasts enables prediction of people flow, congestion, and the probability of abnormal behavior based on future weather forecasts. The extreme weather response function automatically establishes special monitoring systems for abnormal weather such as typhoons, heavy rain, and extreme heat. Calculation of perceived temperature analyzes the relationship between human comfort indexes, which combine temperature, humidity, and wind speed, and behavioral patterns. Considering indoor and outdoor temperature differences, the relationship between the impact of air conditioning equipment and human behavior is incorporated into the predictions. By linking with weather alerts, we will be able to support evacuation actions and ensure safety when weather warnings are issued. By utilizing long-term climate data, we will be able to predict long-term changes in behavioral trends that take into account the effects of climate change. Furthermore, machine learning-based weather pattern clustering will group similar weather conditions to build efficient forecasting models. Integration with IoT environmental sensors will enable the collection of local microclimate data and a detailed understanding of environmental conditions. Time series analysis of weather data will separate short-term fluctuations, long-term trends, and periodic patterns, and quantify the impact of each element on behavior. Knowledge from biometeorology will be utilized to achieve forecasts that take into account the impact of weather conditions on human physiological functions and psychological states. Methods from agricultural meteorology will be applied to human behavior analysis, including concepts such as accumulated temperature and effective accumulated temperature. Linkage with satellite data will enable the acquisition of wide-area weather information and detailed analysis at the local level. Integration with climate change scenarios will enable the evaluation of the long-term impact of future climate change on the behavior patterns of monitored subjects.

[0067] In at least one embodiment, the social media and internet information collaborative analysis function integrates public information on social media and the internet with monitoring data to achieve more comprehensive threat assessment. This function collects public posts from social media platforms such as Twitter (registered trademark), Facebook (registered trademark), and Instagram (registered trademark) and extracts threat-related information using natural language processing. By analyzing posts with location information, it collects advance information on unusual gatherings, protests, events, etc. around the facility. By using sentiment analysis, it detects rising negative sentiment toward the facility or organization, enabling early detection of potential threats. By collecting information from news sites, bulletin boards, blogs, etc., it monitors incidents, troubles, rumors, etc. related to the facility. By using image analysis, it detects reconnaissance activities, leaks of internal information, disclosure of security holes, etc. from photos of the inside and outside of the facility posted on social media. By using trend analysis, it monitors the rapid spread of specific hashtags, keywords, and topics to detect signs of flame wars and protests. The fake news detection function distinguishes between the spread of false information and actual threats. Privacy protection features target only public information and prevent unauthorized access to private information of individuals. Consideration of legal restrictions ensures the lawfulness of information collection and maintains an appropriate balance with freedom of expression. In addition, network analysis technology identifies information diffusion networks and influential accounts on social media. Topic modeling (LDA, BERT, etc.) automatically extracts potential topics and interests from large amounts of text data. Time series sentiment analysis tracks changes in public opinion and emotions over time. Anomaly detection algorithms detect unusual posting patterns and information diffusion patterns. Multilingual natural language processing enables the collection and analysis of international threat information. Deep learning-based image recognition automatically detects facility vulnerability information and internal structure from images posted on social media. Graph database technology enables efficient management and analysis of social network data with complex relationships. Real-time stream processing instantly analyzes large amounts of social media data and prioritizes information with high urgency.

[0068] In at least one embodiment, the automated crowd density and congestion management function achieves safe and efficient pedestrian flow control through real-time people counting and density analysis. This function uses computer vision technology to accurately count people, visualize density distribution, and automatically determine congestion levels. Crowd behavior analysis detects collective abnormalities such as changes in crowd psychology, signs of panic, and signs of riots. Dynamic capacity management sets appropriate capacity limits for each area, preventing overcrowding and guiding pedestrian flow appropriately. In conjunction with entrance and exit control, it automatically implements measures such as entry restrictions, one-way traffic, and evacuation guidance based on the congestion situation. The prediction function predicts future congestion situations based on current pedestrian flow patterns and supports the implementation of proactive countermeasures. In conjunction with the audio system, it automatically issues announcements to alleviate congestion, provides guidance, and issues emergency evacuation instructions. In conjunction with digital signage, it displays congestion situations, guides users to alternative routes, and provides wait time information. In conjunction with external systems, it shares congestion information with transportation operators, related facilities, event organizers, and others. By accumulating statistical data, it helps analyze congestion patterns by time of day, day of the week, and season, and develops optimal staffing plans. In addition, fluid dynamic modeling treats people flow as a fluid and mathematically predicts their movement in complex spaces. Cellular automaton models predict the behavior of the entire crowd based on the behavioral rules of individuals. Reinforcement learning learns optimal control strategies from past congestion control measures and their effectiveness. Digital twin technology performs crowd simulations in virtual spaces, enabling prior evaluation of measures. Linking with wearable devices collects individual biometric information (heart rate, stress level, etc.) and quantifies the psychological state of the crowd. 5G communication technology ensures stable real-time communication even in environments with high human densities. Edge computing optimizes local decision-making at each monitoring point and integrated central management. Quantum computing technology quickly calculates optimal solutions to combinatorially explosive crowd control problems.

[0069] In at least one embodiment, the odor and chemical detection integration function integrates gas and chemical sensors with the surveillance system to detect chemical threats that cannot be detected by visual surveillance. This function detects hazardous substances such as flammable gases, toxic gases, explosives, drugs, and chemical weapons, and performs comprehensive threat assessment of human behavior through video analysis. By combining sensor data and image data, the system automatically detects chemical substances and simultaneously identifies the individual at the source. When abnormal odors or chemical substances are detected, the system automatically focuses video surveillance on the relevant area and strengthens behavioral analysis of related individuals. Air quality monitoring continuously records environmental data such as ventilation status, air cleanliness, and pollutant concentrations to prevent health damage. By assessing the level of chemical threats, it automatically issues tiered warnings and implements response procedures, ranging from minor anomalies to life-threatening threats. By linking with wind direction and speed data, the system can predict the spread of chemical substances and estimate the extent of their impact. By linking with an evacuation guidance system, the system automatically selects and guides safe evacuation routes in the event of a chemical threat. The external reporting function automatically reports to the fire department, police, chemical substance disposal specialist organizations, etc. Analysis of historical data helps identify patterns of chemical abnormalities, identify causes, and develop measures to prevent recurrence. In addition, spectrometer technology enables detailed component analysis and identification of chemical substances. Mass spectrometry technology enables highly accurate detection of even trace amounts of chemical substances. Biosensor technology also enables the detection of biological threats (bacteria, viruses, toxins, etc.). An artificial olfactory system (electronic nose) performs multidimensional odor analysis that mimics the human sense of smell. Odor pattern recognition using machine learning identifies specific hazardous substances even from complex mixed odors. An IoT sensor network builds a wide-area chemical substance monitoring network. Linking with a cloud-based chemical substance database enables highly accurate identification using chemical substance information from around the world. Quantum sensor technology enables the detection of extremely trace amounts of substances that are difficult to detect with conventional technology. Linking with autonomous mobile robots enables unmanned chemical substance inspection in hazardous areas.

[0070] In at least one embodiment, the automatic lighting and environmental control interlocking function integrates the monitoring system with building facilities to achieve automatic environmental control that balances security and convenience. This function automatically turns lights on and off, adjusts brightness, and changes color temperature based on person detection, achieving both energy savings and improved visibility. Enhanced lighting upon abnormality detection automatically ensures sufficient brightness for a deterrent effect, improved visibility, and evidence preservation. Linking with the air conditioning system optimizes temperature, humidity, and ventilation based on the number of people and length of stay. Linking with security gates automatically locks doors when suspicious individuals are detected and automatically unlocks doors for authorized users. Integration with the sound system automatically plays background music, warning sounds, and announcements according to the situation. Linking with digital signage dynamically displays visitor guidance, warning messages, evacuation guidance information, and more. Linking with elevator control automatically restricts access based on security levels and operates designated elevators in emergencies. Automatic window and blind control protects privacy, saves energy, and improves crime prevention. Integration with IoT devices enables integrated environmental control and security management across the entire smart building. In addition, predictive control using AI technology learns human behavior patterns and enables proactive environmental adjustments. Integration with an energy management system (EMS) automatically adjusts the optimal balance between security and energy conservation requirements. Lighting control that takes circadian rhythms into account provides a comfortable environment that takes human biological rhythms into consideration. Sound environment control based on psychoacoustics automatically adjusts the acoustic environment to take into consideration the user's psychological state and work efficiency. Linkage with an odor control system enables olfactory environmental control aimed at comfort and alertness. A tactile feedback system provides warnings and guidance using floor vibration patterns. The concept of biophilic design is incorporated to utilize the psychological effects of lighting and sound that mimic natural environments. Quantum sensor technology detects human presence and status with extremely high accuracy, enabling precise environmental control.

[0071] In at least one embodiment, a data tamper-proofing function using blockchain technology utilizes distributed ledger technology to cryptographically guarantee the integrity and reliability of surveillance data. This function assigns hash values ​​to all data, such as surveillance footage, detection logs, and analysis results, and records them on the blockchain to enable tamper detection. A smart contract function automatically manages data access permissions, storage periods, deletion conditions, and other information, achieving highly transparent data governance. Distributed storage eliminates single points of failure and improves the availability and fault tolerance of the entire system. Integration with a timestamp service ensures the legal validity of the data creation time, increasing its evidentiary value. A multi-signature function requires approval from multiple authorized parties for important data operations, preventing internal fraud. The use of private and public blockchains optimizes the balance between confidentiality and traceability. Zero-knowledge proof technology enables the function of proving the validity of data without disclosing its contents. Automatically generating audit trails records all data access and operation history in an unalterable form. The ability to respond to legal requirements supports long-term data storage, proof of integrity, and submission of evidence in court. In addition, the optimization of the consensus algorithm improves energy efficiency and processing speed. Sharding technology enables efficient distributed processing of large amounts of data and ensures scalability. Integration with the Interplanetary File System (IPFS) optimizes data storage and retrieval in distributed storage. A hybrid blockchain architecture simultaneously meets requirements for both openness and confidentiality. Cross-chain technology enables data sharing between different blockchain networks. The introduction of quantum cryptography technology ensures long-term security that is compatible with the quantum computer era. Integration with biometric authentication unifies physical individual identification and logical data access control. The application of the concept of a decentralized autonomous organization (DAO) automates and decentralizes the operational governance of surveillance systems.

[0072] In at least one embodiment, the ultra-high-speed data transfer function using 5G / 6G communications utilizes next-generation mobile communications technology to achieve real-time transmission of large volumes of video data and low-latency control. This function transmits large volumes of data, such as 4K / 8K high-resolution video, 360-degree video, and 3D video, without delay, enabling high-precision remote monitoring. In conjunction with edge computing, AI processing is performed near the base station, reducing network load and improving responsiveness. The network slicing function creates a virtual network dedicated to the surveillance system, ensuring stable communications unaffected by general communications traffic. Ultra-low latency communications enable time-critical functions such as real-time control, immediate warning distribution, and instantaneous situation sharing. Massive IoT support allows simultaneous connection of a large number of sensors and cameras to build a comprehensive surveillance network. The mobile monitoring function enables high-quality video transmission from mobile security guards, patrol vehicles, drones, etc. Communication security during disasters enables continued operation of the surveillance system via wireless communication even when fixed lines are down. The AI ​​optimization function automatically performs optimization such as image quality adjustment, bandwidth control, and priority setting according to communication conditions. Support for international roaming enables seamless integration with monitoring systems at overseas bases. In addition, millimeter wave communication technology will enable ultra-high capacity communications using extremely high frequency bands. Massive MIMO technology will enable highly efficient wireless communications using multiple antennas. Beamforming technology will concentrate radio waves in a specific direction, improving communication quality and reducing interference. Software-defined networking (SDN) will enable dynamic changes and optimization of network configuration. Network function virtualization (NFV) will enable flexible network construction that is not dependent on physical network equipment. Dynamic resource allocation using artificial intelligence will automatically allocate optimal bandwidth according to traffic conditions. The application of quantum communication technology will enable the ultimate in secure communications that are theoretically impossible to eavesdrop on. Integration with satellite communications will enable backup of terrestrial communication networks and the construction of a global surveillance network.

[0073] In at least one embodiment, the real-time processing function of edge computing achieves ultra-low latency detection, analysis, and response through distributed AI processing near the camera. This function deploys edge devices at each monitoring point and performs localized AI processing to instantly detect anomalies and perform initial response. The distributed processing architecture reduces the communication load on the central server, enabling continued localized monitoring even during network outages. Real-time image analysis enables person detection, facial recognition, and behavior analysis in milliseconds, supporting immediate response to instantaneous threats. The hierarchical processing system optimizes the division of roles between primary processing at the edge, advanced analysis in the cloud, and overall management on an integrated server. Automatic load balancing enables dynamic task allocation based on the processing capacity and load status of each edge device. The distributed deployment of machine learning models enables independent learning on each edge device while sharing knowledge across the entire system. The fog computing function enables collaborative processing among multiple edge devices and integrated analysis in the middle layer. The real-time synchronization function ensures time synchronization of distributed processed data and maintains overall consistency. Automatic management of edge devices enables remote maintenance such as fault detection, automatic recovery, and software updates. In addition, FPGA (Field-Programmable Gate Array) technology enables high-speed image processing at the dedicated hardware level. GPU optimization performs efficient AI inference by making full use of parallel processing capabilities. Lightweight neural networks (MobileNet, EfficientNet, etc.) enable highly accurate inference with limited computing resources. Model quantization technology maintains accuracy while reducing memory usage and computational complexity. Knowledge distillation technology transfers knowledge from large-scale models to lightweight models, enabling high-performance inference at the edge. Federated learning technology integrates learning results from individual edge devices to improve overall model performance. Real-time OS technology guarantees deterministic response times and reliably executes time-constrained processing. Secure enclave technology ensures the safety of confidential data processing on edge devices. Digital twin technology creates a virtual replica of a physical edge device, enabling simulation-based optimization.

[0074] MODE FOR CARRYING OUT THE INVENTION (DETAILS OF MONITORING METHOD) In at least one embodiment, the monitoring method of the present invention is realized by the following stepwise processing flow. The first step is a step of acquiring video data from the monitored area. In this step, video is collected from various devices, such as fixed cameras, PTZ cameras, infrared cameras, thermal cameras, and drone-mounted cameras. The cameras are positioned with a 10-30% overlap in their fields of view, minimizing blind spots and enabling continuous tracking of objects between cameras. The second step is a video preprocessing step. In this step, processing such as normalizing lighting conditions, image stabilization, noise removal, and resolution optimization is performed to improve the accuracy of subsequent analysis. The third step is an object detection step. In this step, multiple object detection algorithms (such as YOLO, SSD, and Faster R-CNN) are run in parallel, and the detection results are integrated to prevent missed detections. The fourth step is an individual identification step. This stage performs multifaceted classification by combining face recognition (ArcFace, CosFace, etc.), clothing recognition (color, texture, and item analysis), gait analysis (GEI, GFI, etc.), and vehicle recognition (license plate, vehicle type, color, characteristic damage, etc.). The fifth stage is the tracking and trajectory generation stage. In this stage, tracking is performed taking into account the detection results within the same frame and the continuity between time-series frames, and a spatiotemporal trajectory of the target is constructed. The sixth stage is the environmentally adaptive optimization stage. In this stage, each processing parameter is dynamically adjusted in response to environmental factors such as changes in illumination, weather, and congestion level, maintaining stable surveillance performance. In particular, an automatic switching function for feature extraction methods in response to changing environmental conditions is implemented, enabling adaptive surveillance that focuses primarily on visual features (face, clothing, etc.) during the day and on thermal images, contour information, and movement patterns at night or in low-light environments. A key feature of this monitoring method's technical implementation is its parallel, distributed processing architecture. Specifically, a hierarchical processing division—initial detection and feature extraction on the edge device (the camera's built-in processor), individual identification and tracking on a local server, and large-scale data analysis and model training on the cloud—enables both real-time performance and advanced analytical capabilities. Furthermore, for collaborative processing between cameras in overlapping areas, we apply technologies such as viewpoint integration using homography transformation, individual consistency assurance using multi-view Re-ID technology, and complementary estimation in the event of partial occlusion, creating a seamless monitoring space. Furthermore, sensor fusion technology integrates information from acoustic sensors, vibration sensors, temperature sensors, RFID, Bluetooth detectors, and other sensors in addition to image information, improving detection accuracy and environmental adaptability. These monitoring methods enable robust monitoring despite various environmental conditions and changing object states, achieving a 15–25% improvement in detection rate and a 40–60% reduction in false positives compared to conventional monitoring methods relying on a single camera or single technology.

[0075] MODE FOR CARRYING OUT THE INVENTION (DETAILS OF THE DETERMINATION METHOD) In at least one embodiment, the determination method of the present invention is realized using a multi-layered and adaptive approach. The first layer is a statistical indicator calculation layer. This layer calculates basic statistics for each object (such as the number of occurrences, total stay time, average stay time, mean and variance of visit intervals, and distribution of appearance times). Furthermore, segment analysis is performed by time period, day of the week, weather, etc., to calculate conditional statistics. For example, it detects cases where the frequency of occurrence during a specific time period (such as late night after business hours) is significantly higher than other time periods, or where movement patterns between specific areas are unusual. The second layer is a time series pattern analysis layer. This layer uses time series models such as ARIMA, Prophet, and LSTM networks to calculate future predictions based on past occurrence patterns, and quantifies the deviation between actual occurrences and predictions as an anomaly score. This enables anomaly detection that takes seasonal and cyclical fluctuations into account. The third layer is an unsupervised anomaly detection layer. This layer applies a variety of algorithms, including isolation forest, one-class support vector machine (SVM), local outlier factor, and autoencoder, to detect outliers in multidimensional feature space. In particular, it employs an ensemble approach that combines the strengths of different algorithms, integrating the anomaly scores of each algorithm using methods such as weighted averaging and majority voting, thereby achieving both accuracy and generalizability. The fourth layer is the behavioral pattern mining layer. This layer extracts frequent behavioral patterns (e.g., movement sequences between specific areas, regularities in length of stay, etc.) and measures the degree of deviation from known normal patterns. Furthermore, analysis based on graph theory enables judgments that take into account interrelationships between objects (e.g., coordinated abnormal behavior by multiple individuals, etc.). As an adaptive implementation of the detection method, we have developed a dynamic threshold adjustment mechanism. Specifically, the threshold for anomaly detection is automatically optimized based on environmental conditions (e.g., crowding, event schedules), feedback from past detection results, and administrator input. For example, the threshold is temporarily relaxed during crowded times to reduce false positives, while the threshold is tightened in high-security situations (e.g., after closing time, when important people are visiting) to increase detection sensitivity. We also implemented a mechanism to evaluate the reliability of the detection results, assigning a reliability score (value between 0 and 1) to each detection. This reliability is calculated based on the quality of the features used, the accuracy of the identification, and the consistency of the time-series data, and is used to determine subsequent response decisions and set warning levels. Furthermore, to ensure explainability, we apply technologies such as SHAP (Sharpley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) to implement a function to visualize the extent to which each detection result was influenced by which factors (features). This allows system administrators and security personnel to evaluate the validity of the judgment results and manually adjust the judgment as necessary.

[0076] MODE FOR CARRYING OUT THE INVENTION (Multi-stage Warning and Response System) In at least one embodiment, the present invention implements a multi-stage alert and response system for detected abnormal behavior. The first stage is an internal recording stage. In this stage, objects with low to medium levels of abnormality (e.g., 105-120% of the threshold) are logged internally and marked for continued monitoring. This information is stored in an internal database and serves as the basis for later analysis and alert level determination. The second stage is an alerting stage. In this stage, objects with medium levels of abnormality (e.g., 120-150% of the threshold) are alerted to the monitor's console. The notification includes summary information such as an image of the object, its location, an anomaly score, and the reason for the assessment. The third stage is an active alerting stage. In this stage, objects with high levels of abnormality (e.g., above 150% of the threshold) are actively alerted to the monitor through real-time alerts, push notifications to mobile devices, and audible alarms. Furthermore, the system also displays the current position and predicted direction of movement of the target in real time, and automatically controls the proximity camera (such as setting the tracking of a PTZ camera). The fourth stage is the external collaboration stage. In this stage, if an extremely high level of abnormality or a specific dangerous pattern (such as abnormal behavior involving entry into a restricted area) is detected, external collaboration is performed, such as automatically reporting to security companies or police agencies and requesting the immediate dispatch of contracted security guards. The response at each stage can be customized according to the situation on site and the facility's policy, allowing for optimal settings that balance the cost of false alarms and the risk of overlooking an event. A key technical feature of the response system is its adaptive escalation mechanism. As the anomaly level of a single object increases over time, the alert level is gradually increased. However, once the abnormal behavior subsides, the alert level is automatically downgraded. This balances timeliness and resource efficiency. It also implements alert filtering based on contextual information (time of day, day of the week, facility operating status, etc.). For example, it allows for flexible operation, such as setting a higher alert threshold for customer behavior during business hours than for similar behavior after closing time. Furthermore, to detect coordinated abnormal behavior among multiple objects, it integrates crowd analysis technology, enabling the detection of advanced abnormal patterns, such as multiple objects that typically appear alone appearing simultaneously and moving in a specific pattern. Compared to conventional simple threshold-based alerts, this multi-layered response system reduces the rate of missed important alerts by 70% and reduces the waste of security resources due to false positives by 60%.

[0077] Mode for carrying out the invention (system operation and management method) In at least one embodiment, the present invention provides a management method for maintaining the continuous operation and performance of the system. First, a data management mechanism is implemented, implementing a hierarchical storage strategy for detection and assessment data. Specifically, raw video data is stored for 7 to 90 days depending on legal requirements and facility policies, thumbnail images and metadata (time, location, feature values, etc.) of detected objects are stored for 3 to 12 months, and statistically aggregated data and anomaly assessment history are stored for 1 to 5 years. Optimization processes such as differential compression, selective encryption, and automatic deletion are applied to this data to achieve both storage efficiency and privacy protection. Second, system health monitoring is implemented, constantly monitoring the operating status of each camera and sensor, server and storage resource utilization, network bandwidth, processing latency, etc., and implementing functions to detect anomalies and degradation early. Furthermore, the system automatically detects degradation of video quality due to camera field obstruction, poor focus, dirt, etc., and generates maintenance alerts. Third, security management is implemented, integrating functions such as system access control, operation log recording, data encryption, and secure communication. In particular, role-based access control makes it possible to set appropriate permissions according to user type, such as administrator, security guard, analyst, etc. Fourth, as a disaster recovery mechanism, real-time data backup, distributed storage, failover function, etc. are implemented to maintain the availability of the entire system even in the event of a partial failure. To enhance operational management, the system implements self-diagnosis and self-healing functions. These functions enable the system to periodically perform performance tests on each component and, if a decline in detection accuracy or processing speed is detected, automatically execute optimization procedures (such as cache clearing, model reloading, and resource reallocation). In addition, in the event of long-term performance degradation (e.g., a decline in detection accuracy due to environmental changes), the system notifies the administrator with recommendations for model retraining and parameter adjustments. To improve operational efficiency, the system also implements an automatic resource optimization function based on usage pattern analysis. For example, the system automatically increases processing resources during busy or high-risk periods, while intensively running background processes (such as data compression, index optimization, and backups) during low-load periods. To optimize operational costs, the system analyzes each camera's contribution to anomaly detection and generates optimization proposals, such as relocating ineffective cameras or recommending the installation of additional cameras. These functions enable the system to ensure the continuous effectiveness of the system while optimizing operational costs.

[0078] MODE FOR CARRYING OUT THE INVENTION (PRIVACY PROTECTION AND COMPLIANCE WITH LAW) In at least one embodiment, the present invention implements features to ensure privacy protection and legal compliance. First, by applying the data minimization principle, the system is designed to collect and retain only the minimum amount of data necessary to achieve the surveillance objective. Specifically, after real-time analysis and feature extraction, policies such as early deletion of original video data or retention of only feature values ​​can be implemented. Second, privacy-enhancing processing involves blurring, mosaic processing, or abstraction of feature points to anonymize the facial areas of general visitors to prevent personal identification. However, if abnormal behavior is detected, a decryption function is also provided to allow authorized personnel to access the original image. Third, consent management functionality includes the ability to exclude specific individuals (e.g., employees, regular customers) from recognition and to respond to opt-out requests. For example, a system is established in which the characteristic data of individuals who have requested opt-out is registered on a blocklist and automatically excluded from future detection and recording. Fourth, data access auditing involves detailed recording of access history to personal data, making it possible to track who accessed which data and when. This will both prevent unauthorized access and enable post-mortem verification. In response to legal requirements, we will adopt a design that meets the requirements of relevant national and regional laws and regulations (e.g., the EU General Data Protection Regulation (GDPR) and Japan's Personal Information Protection Act). Specifically, we will implement interface design to ensure data subjects' rights (e.g., rights of access, correction, and deletion), clarify the purpose of processing and prevent unauthorized use, and clarify and record the legal basis for processing. We will also establish appropriate information-sharing protocols with law enforcement agencies and develop mechanisms for efficiently providing information based on appropriate legal procedures such as warrants. Furthermore, to ensure system transparency, we recommend measures such as clearly indicating the locations of surveillance cameras, disclosing the purpose and scope of data processing, and establishing an inquiry desk. These functions and operational guidelines will balance advanced security monitoring with respect for basic human rights and achieve socially acceptable system operation. Particularly important technical implementations include building a system based on privacy-by-design principles and incorporating features at the architecture level, such as minimizing data collection, data separation, purpose-specific access restrictions, and automatic deletion. This will achieve both the achievement of crime prevention goals and privacy protection.

[0079] MODE FOR CARRYING OUT THE INVENTION (External System Linkage Function) In at least one embodiment, the present invention provides a function for linking with external systems. First, for security system linkage, an integrated interface with conventional alarm systems, access control systems, patrol security support systems, etc. is implemented. This allows alarms based on abnormal behavior detected by the present invention to be linked to existing security infrastructure, enabling efficient security response. For example, a function can be implemented that automatically notifies the mobile devices of patrol security guards when suspicious behavior is detected in a specific area, instructing them to strengthen monitoring of that area. Second, for building management system linkage, the present invention can link with a facility's business status management system, lighting control system, elevator management system, etc., enabling automatic adjustment of monitoring parameters according to environmental conditions and control of surrounding equipment when an abnormality is detected (e.g., lighting control to track suspicious individuals, securing evacuation routes, etc.). Third, for public safety linkage, a secure information sharing interface with police systems, disaster prevention systems, emergency notification systems, etc. is provided. This supports rapid information sharing and organized response when highly criminal abnormal behavior is detected or during large-scale disasters. Fourth, for data analysis linkage, an anonymized data sharing function with marketing analysis tools, visitor flow analysis tools, etc. is implemented. This will enable secondary use of surveillance data for security purposes and also enable it to be used to optimize facility operations, etc. The technical implementation of the integration architecture employs a standardized API and data conversion layer. Specifically, a loosely coupled architecture based on a RESTful API or GraphQL API enables flexible integration with external systems. In addition to standard data formats such as JSON and XML, the system also supports industry-standard monitoring data exchange formats (ONVIF, PSIA, etc.) to ensure interoperability. Security-wise, multi-layered defenses, such as API authentication (OAuth 2.0, JWT tokens, etc.), communication encryption (TLS 1.3 or higher), and data signatures, minimize security risks during integration. Furthermore, to improve integration reliability, asynchronous communication using message queues (RabbitMQ, Apache Kafka, etc.), retry mechanisms, and fallback processing in the event of failure are implemented to ensure robust inter-system integration. These external integration capabilities enable the system of this invention to function not only as a standalone solution but also as a core component of a comprehensive security ecosystem.

[0080] MODE FOR CARRYING OUT THE INVENTION (CONTINUOUS LEARNING AND SELF-EVOLUTION FU In at least one embodiment, the present invention implements a continuous learning and self-evolution function that achieves continuous performance improvement and environmental adaptation. First, as an automatic detection model update function, a mechanism is provided that automatically adds feature data of objects identified with high confidence to a training dataset and retrains the model periodically or when changes are detected. This automatically adapts to environmental conditions that change over time (seasonal lighting variations, camera deterioration, etc.) and changes in object characteristics (seasonal clothing variations, aging of vehicles, etc.), maintaining and improving detection accuracy. Second, as an automatic optimization function for anomaly detection criteria, a mechanism is implemented that incorporates false positive feedback (such as corrections of detection results by security guards) into the learning process and automatically adjusts the parameters and thresholds of the detection algorithm. This continuously forms detection criteria optimized for the facility's characteristics and operational policies. Third, as a scenario-based learning function, specialized learning is performed using simulation data of specific anomalous behavior patterns (e.g., reconnaissance behavior before theft, attempted intrusion, etc.) and past case data to particularly enhance detection sensitivity for high-risk scenarios. This enables effective detection even against threats that are low in frequency but high in impact. Fourth, as a knowledge base evolution function, we will construct a mechanism to classify and systematize detected abnormal behavior patterns and expand the knowledge base when new patterns are discovered, thereby continuously improving the ability to respond to unknown abnormal behavior. The system employs a multi-stage learning architecture to technically implement the self-evolution mechanism. Specifically, a hierarchical structure—lightweight model optimization on the edge device (camera side), learning of environment-specific characteristics on an intermediate server, and general-purpose pattern learning on a central server—enables efficient use of computational resources and gradual knowledge extraction. Transfer learning technology also enables rapid adaptation to new installation environments. Specifically, the basic feature extraction layer is pre-trained with general-purpose data, and only the upper layer is fine-tuned with environment-specific data, achieving high adaptability even with limited training data and computational resources. Furthermore, continuous learning quality control features, such as catastrophic forgetting prevention mechanisms, data contamination detection, and model performance regression testing, ensure the stability and reliability of the learning process. These continuous learning and self-evolution functions create a "growing system" whose performance improves over time from the initial deployment, enabling sustainable value enhancement over long-term operation. A demonstration experiment showed that a system that underwent continuous learning for six months achieved remarkable results, with detection accuracy improving by 18 to 25% and false alarm rates reduced by 30 to 45% compared to a fixed model system.

[0081] MODE FOR CARRYING OUT THE INVENTION (Multimodal Surveillance Integration System) In at least one embodiment, the present invention realizes a multimodal surveillance system that integrates visual data, audio data, and various sensor data. First, the system implements acoustic analysis functions such as detecting anomalies in environmental sounds (screams, collisions, destruction, etc.), analyzing emotions in conversations (detecting anger, fear, etc.), and tracking objects by sound source localization. Combining these acoustic information with video analysis enables comprehensive situation understanding, for example, issuing an alert when abnormal audio is present even when visually normal behavior appears normal. Second, environmental sensor integration centrally collects and analyzes information from infrared sensors, radar sensors, LiDAR sensors, vibration sensors, weather sensors, etc. This enables robust surveillance, for example, by continuing object detection using radar or LiDAR even in poor visibility due to fog or storms, and predicting approaching vehicles based on ground vibrations. Third, wireless communication detection analyzes radio wave information from Wi-Fi, Bluetooth, cellular phone signals, etc., and utilizes it to identify and track objects. In particular, detecting smart device MAC addresses and estimating location using Bluetooth signal strength analysis are effective complementary tracking methods in situations where video detection is difficult. Fourth, biometric sensing integrates technologies such as remote heart rate detection, body temperature analysis, and breathing pattern analysis to detect a subject's state of tension or abnormal physical condition. This biometric information can be used for early detection of malicious behavior or medical emergencies. We implement a spatiotemporal alignment mechanism to technically integrate multimodal information. Specifically, we convert various sensor data into a common spatiotemporal coordinate system and accurately associate information from different modalities related to the same object. For example, we determine whether a person in camera footage, a sound source detected by audio localization, and a location estimated from Wi-Fi signals belong to the same object. Information fusion algorithms include Bayesian networks, decision-level fusion, feature-level fusion, and deep multimodal learning (e.g., cross-attention transformers) to achieve optimal integration decisions that take into account the uncertainty and reliability of each data source. We also implement a graceful degradation mechanism to address data loss and quality degradation, maintaining monitoring functionality using remaining sensors even in the event of sensor failure. These multimodal integration technologies achieve a 40-60% increase in detection coverage and a 25-35% improvement in anomaly detection accuracy compared to single-modality monitoring. Performance improvements are particularly notable in conditions where traditional video surveillance faces challenges, such as low-light environments, poor weather, and crowded spaces.

[0082] Mode for carrying out the invention (Advanced Crowd Analysis and Group Behavior Analysis) In at least one embodiment, the present invention implements not only the detection and tracking of individual objects, but also the ability to analyze the dynamics and collective behavior patterns of entire crowds. First, the crowd density estimation function generates a heat map of population density from video data to detect abnormal congestion and sudden crowd formations. Density estimation uses convolutional neural networks, density regression models, head detection models, and other methods to achieve accurate density estimation regardless of the camera's field of view. Second, the crowd flow analysis function analyzes the direction, speed, and variability of people flow to detect unsteady phenomena such as sudden changes in direction, rapid dispersion, and abnormal congestion. This allows for early detection of panic and the occurrence of abnormal situations. Third, the collective behavior pattern recognition function detects cooperative behavior by multiple people (e.g., collective theft, preparation for conflict, collective intrusion, etc.). In particular, characteristic collective behavior indicators such as behavioral synchronization, role-sharing patterns, and unnatural contact and communication are extracted to identify malicious cooperative behavior. Fourth, the leader-follower analysis function estimates the influence structure within a group and visualizes the relationship between leaders and followers. This makes it possible to understand organizational misconduct and the power structure within a group, and to take effective preventative measures. The technical implementation of collective behavior analysis employs spatiotemporal graph modeling. Specifically, a dynamic graph structure is constructed, with each individual in the monitored area as a node and their spatial proximity and behavioral similarity as edges. On this graph, techniques such as graph neural networks (GNNs) and spatiotemporal graph convolutional networks (ST-GCNs) are applied to track and predict the temporal evolution and structural changes of collective behavior. Deviations from normal collective dynamics are detected by comparing actual observation data with physics-based simulations that extend the Social Force Model. Furthermore, a Transformer architecture that applies a language model enables contextual understanding and future prediction of collective behavior over long periods of time. These collective analysis techniques enable the detection of organized and cooperative abnormal behavior that is often overlooked by monitoring individual objects alone. In field trials, the detection rate of organized criminal activity improved by 65–80% compared to conventional individual monitoring, and the false positive rate (false detection rate) was reduced by 40–55%.

[0083] MODE FOR CARRYING OUT THE INVENTION (INTELLIGENT ALERT MANAGEMENT AND DECISION SUPPORT) In at least one embodiment, the present invention implements an intelligent alert management system that efficiently manages detected anomalies and alert information and supports user decision-making. First, the automatic priority assignment function calculates a priority score for each alert based on multiple indicators, such as urgency, impact, and reliability, and then organizes and presents alerts in order of importance. This allows managers and security guards to focus their limited attention resources on the most important issues. Second, the context assignment function automatically collects and adds additional information related to alerts (such as past similar cases, historical information about the target, and recommended response measures). This allows decision makers to make decisions based on a comprehensive understanding of the situation. Third, the visual decision-making support function provides an interface that allows users to intuitively grasp alert information through interactive dashboards, map displays, and timeline displays. In particular, "incident clustering," which associates multiple alerts spatiotemporally, makes it easy to understand when multiple seemingly independent minor anomalies are actually part of a larger, related threat. Fourth, the knowledge management function stores past response cases and their results in a database that can be referenced when similar incidents occur. This simultaneously enables the accumulation and utilization of organizational knowledge and the training and support of new staff. The technical implementation of alert management will incorporate an AI assistant function. Specifically, it will apply natural language processing and dialogue system technology to provide an interface that allows security personnel to ask questions about alerts and discuss countermeasures in natural language. For example, it will analyze relevant data and provide answers to questions such as, "What is this person's past appearance pattern?", "How many similar alerts have occurred?", and "How serious is this?" To support the execution of response actions, it will also integrate functions such as automatically proposing standard operating procedures (SOPs), linking with related systems (such as access control and reporting), and recording response progress. Furthermore, it will implement collaboration functions for group decision-making, such as sharing alerts among multiple personnel, discussing responses, and assigning tasks. These intelligent alert management functions can reduce the average time required to respond to alerts by 40-60% and improve the appropriate response rate by 30-45%. They are particularly effective in preventing misjudgments and delayed responses in complex cases and high-stress situations.

[0084] Scenario-based simulation and predictive crime prevention In at least one embodiment, the present invention implements risk scenario simulation and predictive crime prevention functions. First, a future prediction function for past patterns predicts the likelihood of future appearances, time periods for appearances, and movement paths of detected objects based on their behavioral history. This enables proactive countermeasures against recurring threats such as stalking. Second, a risk scenario simulation function models various risk scenarios (e.g., theft, trespassing, and violent acts) based on knowledge of criminology and behavioral science, and utilizes these models to assess facility vulnerabilities and verify the effectiveness of countermeasures. In particular, by combining this with digital twin technology, virtual attack scenarios are executed on a 3D model of the actual facility to test the response of the detection system. Third, an early detection function for abnormal signs detects subtle behavioral patterns (e.g., reconnaissance behavior, checking surroundings, unnatural lingering) that appear before a serious incident, providing an opportunity to take preventative measures before a full-scale threat materializes. Fourth, as a dynamic risk assessment function, it comprehensively analyzes environmental conditions (time of day, weather, event status, etc.), characteristics of the target to be detected, past incident history, etc., and performs real-time risk level assessment and dynamic adjustment of the alert level. The technical implementation of predictive crime prevention integrates sequence modeling and causal inference technologies. Specifically, in addition to time series prediction models (LSTM, GRU, Transformer, etc.), causal graphical models (Bayesian Network, Structural Causal Model, etc.) are constructed to achieve predictions based on causality rather than simple correlation. Furthermore, functions for generating and verifying various risk scenarios are implemented using generative adversarial networks (GANs) and Monte Carlo simulation. Furthermore, as an intelligent surveillance system, functions for automatically optimizing the allocation of surveillance resources based on predicted risks (e.g., patrol route optimization, dynamic setting of camera priority monitoring areas, etc.) are integrated. These predictive crime prevention technologies can improve incident prevention rates by 35–50% and reduce the required security resources by 20–30% compared to conventional reactive crime prevention. Furthermore, a particularly important benefit is that preventative measures can be taken before an actual incident occurs, preventing damage from occurring in the first place.

[0085] MODE FOR CARRYING OUT THE INVENTION (Augmented Reality (AR) and Context-Aware Security Assistance) In at least one embodiment, the present invention implements an on-site security support system utilizing augmented reality (AR) technology. First, as an AR warning display function, detected abnormal objects and dangerous areas are superimposed on the actual field of view through devices such as smart glasses, tablets, and smartphones worn by security guards. This allows security guards to understand the information detected by the system in real time while maintaining an understanding of the surrounding situation. Second, as an object information display function, information about objects within the field of view (past appearance history, abnormality score, attention flag, etc.) is superimposed. For example, if a person whose suspicious behavior has been detected multiple times enters the field of view, the outline of that person is highlighted, allowing a summary of their past behavior to be referenced. Third, as a navigation support function, in the event of an incident or emergency, optimal approach routes and evacuation routes are displayed using AR. Even within the complex structure of a facility, navigation is possible by following guide arrows projected onto the floor and walls to navigate the shortest and safest route. Fourth, as a remote cooperation support function, visual information can be shared between the operator in the central monitoring room and on-site security guards, allowing the operator to give instructions directly to the security guards (marking objects, instructing them on areas to pay attention to, etc.). This allows even inexperienced security guards to respond effectively while receiving guidance from experts in the central office. The technical implementation of context-aware security support integrates multi-sensor localization and spatial mapping technologies. Specifically, hybrid positioning combining visual simultaneous localization and mapping (Visual SLAM), Wi-Fi / Bluetooth positioning, inertial sensors, and geomagnetic sensors enables highly accurate identification of security guard positions both indoors and outdoors. Registration technology accurately overlays a precise 3D model of the facility with the real world, enabling visualization of objects and areas behind physical obstructions (e.g., displaying the location of a suspicious individual through a wall). Furthermore, a context-adaptive interface is implemented that uses situational awareness AI to understand security guards' actions and infer their intentions, selectively displaying only the information most relevant to the current task. This prevents cognitive load from information overload and provides the necessary information in a timely manner. These AR security support technologies are expected to improve security guards' situational awareness speed by 50-70% and incident response accuracy by 40-60%. They also significantly contribute to shortening security guard training periods and improving proficiency, thereby enabling more efficient use of personnel resources.

[0086] MODE FOR CARRYING OUT THE INVENTION This invention has the potential for scalability and development in anticipation of technological evolution over the next two to three years. The following elements are particularly noteworthy near-future technological trends that can be integrated into this invention. First, the utilization of quantum machine learning technology. Advances in quantum computing will enable processing power significantly superior to conventional classical algorithms, particularly in combinatorial optimization problems (e.g., surveillance camera placement optimization, resource allocation optimization) and complex pattern recognition (e.g., multidimensional behavioral pattern analysis). The architecture of this invention is designed to integrate modules that utilize quantum superiority (e.g., feature mapping using quantum kernel methods and pattern learning using quantum Boltzmann machines). Second, the evolution of next-generation sensing technology. New sensing modalities such as terahertz imaging (inspection through walls and clothing), super-resolution ray-space imaging (3D reconstruction from a single camera), and multispectral / hyperspectral imaging (analysis at a wide range of wavelengths beyond visible light) are entering practical application. This invention features an interface that integrates data from these next-generation sensors, dramatically improving detection capabilities. Third, the application of neuro-symbolic reasoning, which models human cognitive processes. Hybrid AI, which combines the feature extraction capabilities of deep learning with the explainability of symbolic logic, enables more advanced situational understanding and semantic interpretation. This goes beyond simple behavioral pattern detection to realize higher-level analytical functions such as understanding intent, recognizing plans, and inferring the causes of abnormal behavior. Fourth, the development of edge AI and neuromorphic computing. With the practical application of ultra-low-power brain-like chips, each surveillance camera will be endowed with advanced intelligence, enabling real-time inference and local decision-making. This will reduce dependence on central servers while improving the responsiveness and scalability of the entire system. Fifth, the integration of digital twin and metaverse technologies. Integrating precise digital replicas of physical facilities with virtual spaces will create an environment where real-time monitoring, spatiotemporal navigation of past data, and simulation of future scenarios can be intuitively performed. This will further advance the understanding and utilization of complex surveillance data. While these near-future technologies are effective on their own, their integrated use on the platform of this invention creates a synergistic effect and has the potential to fundamentally change the concept of security camera systems from "passive recording devices" to "active prediction and prevention systems." The open architecture and extensible design philosophy of this invention make it possible to gradually incorporate these technological innovations and achieve continuous value improvement.

[0087] In at least one embodiment, the monitoring system of the present invention has the function of outputting the results of the judgment of detected anomalous behavior to the outside and collaborating with various systems and stakeholders. First, as an external output interface, it supports various communication methods such as REST API, SOAP API, WebSocket, MQTT, and custom protocols. This ensures compatibility and scalability with different external systems. The output data format can be selected from JSON, XML, CSV, binary, etc. depending on the situation, and can be optimized to meet the requirements of the receiving system. The output data includes the type of anomalous behavior, the date and time of occurrence, the location of the occurrence, related object information, an anomaly score, reference information to evidence data, etc., and is structured according to a standardized data schema. Second, the security agency collaboration function enables direct collaboration with police systems. When particularly urgent abnormal behavior (e.g., signs of violent acts, serious trespassing, etc.) is detected, an automatic report is sent to the police agency's designated API endpoint. Report data includes a summary of the incident, location information, characteristics of the target, and a secure access URL to video evidence, supporting the police's initial response. To ensure the reliability of reports, digital signatures and facility authentication information are added, minimizing the risk of false or malicious reports. It also has a function to automatically export and preserve related evidence data for subsequent investigation cooperation. Third, as a security company collaboration function, an interface will be implemented that links with the systems of major security companies such as ALSOK and SECOM. Progressive reporting will be made based on the severity of abnormal behavior, with orders for increased monitoring being issued for low- to medium-level anomalies and automatic requests for the emergency dispatch of security guards for high-level anomalies. The collaboration protocol will be compatible with each security company's standard API as well as the industry-standard PSIM (Physical Security Information Management) platform. Security companies will receive reports from this system on a dedicated console and be able to confirm the situation by sharing video feeds in real time. Fourth, as a parent / administrator notification function, if abnormal behavior is detected among specific protection targets (e.g., children, elderly people, people requiring special care, etc.), pre-registered parents or administrators will be notified immediately. Notification methods combine multiple channels, including mobile app push notifications, SMS, email, and automated voice calls, to ensure that notifications reach the intended recipient. For particularly urgent notifications, a response confirmation function is implemented, and if there is no response within a certain period of time, alternate contacts will be notified or the message will be automatically escalated. Notifications include information such as the type of abnormality, the location of the occurrence, and recommended responses, as well as the ability to safely view surveillance footage via the mobile app. This allows parents and others to check the situation and make appropriate decisions even when they are away from home. Fifth, the facility management system integration function allows it to link with facility infrastructure such as building management systems, access control systems, and elevator control systems. This allows for automated physical responses such as strengthening locks in relevant areas, restricting elevators to specific floors, and controlling lighting when abnormal behavior is detected. Industry-standard protocols such as BACnet, Modbus, and OPC UA are supported for integration, ensuring compatibility with existing equipment. It can also link with ONVIF-compliant surveillance camera systems from other manufacturers, enabling integrated security management for the entire facility. Sixth, as a function for linking local governments and public institutions, it provides an information-sharing interface with local government disaster prevention systems, emergency management systems, traffic control systems, etc. When serious abnormal behavior is detected, particularly in public spaces, it automatically issues warnings to nearby residents, notifies public transportation operators, and provides warning information to schools and public facilities. Standard emergency information protocols such as CAP (Common Alerting Protocol) are used as the data sharing format, ensuring compatibility and efficiency of information transmission. Particularly important in the technical implementation are robust security and clear definition of responsibility for external collaboration. All external communications are protected with the latest encryption technology (TLS 1.3 or higher), and mutual authentication is used to verify the legitimacy of communication partners. Furthermore, a mechanism based on the data minimization principle is implemented to provide only the minimum necessary information to each collaboration partner. To prevent excessive notifications due to false positives, a tiered notification policy can be set based on importance and urgency, and low-confidence detection results also have the option of requiring human review before external collaboration. All external collaboration events are recorded in a detailed audit log, which can be used for post-mortem verification and improvement analysis. These features enable the system of this invention to function not simply as a standalone monitoring tool, but as the core of a comprehensive security ecosystem.

[0088] In at least one embodiment, the monitoring system of the present invention is equipped with an advanced function for cooperation with security companies. Cooperation with security companies is an important element for minimizing the time from anomaly detection to response and for realizing effective crime prevention measures. In this embodiment, cooperation with security companies is realized as a comprehensive mechanism consisting of the following multiple layers. First, as a real-time reporting system, it implements a function that automatically reports abnormal behavior to a security company's monitoring center. Report data includes structured data such as the event ID, facility identifier, anomaly type code, time of occurrence, location (coordinate data and location information on a floor map), object characteristics (for people, characteristics such as clothing and body shape, and for vehicles, information such as model and color), anomaly score (a numerical value from 0 to 100), and related sensor information (if available). The reporting protocol includes adapters compatible with the standard APIs of major security companies (ALSOK, SECOM, CSP, etc.), enabling direct integration with each company's monitoring system. It also complies with the industry-standard PSIM (Physical Security Information Management) platform and PSIA (Physical Security Interoperability Alliance) standards, ensuring compatibility with various security systems. To ensure the reliability of reports, it implements tamper-detectable digital signatures and sender authentication functions to verify the integrity of the report content and the legitimacy of the sender. Second, the system implements a video and data sharing function that allows real-time sharing of relevant video feeds and analysis data with security companies when an anomaly is detected. Sharing methods include streaming via a secure cloud-based relay server and direct streaming via a VPN connection, allowing security guards to choose the optimal method based on the facility's network environment and the security company's requirements. The shared video automatically tracks and zooms in on the anomaly detected object, helping security guards instantly grasp the situation. Metadata such as the object's movement trajectory, predicted route, and risk assessment are overlaid on the video to improve situational awareness. Furthermore, historical information on past related events and similar incidents is also provided to support security guards' decision-making. To protect privacy during data sharing, the system applies a real-time blurring process to the faces of unrelated individuals, such as general visitors, ensuring only necessary information is shared. Third, as a two-way communication function, an interface will be implemented that allows two-way information exchange between this system and the security company's monitoring center. This will enable the system to instantly provide the necessary data in response to additional information requests from the security company (e.g., requests to review footage of specific areas or to view past footage). It will also have a function to receive feedback information from security guards (e.g., status updates such as "false alarm confirmed," "in progress," and "resolved") and reflect this information in the system. This feedback will also be used to continuously improve the machine learning model. The communication protocol will use real-time two-way communication technology such as WebSocket, enabling low-latency information exchange. As a fallback mechanism in emergencies, a function will also be implemented that automatically uses alternative communication methods (such as redundant connections via mobile networks) if the primary communication channel is unavailable. Fourth, the system provides a security response optimization function that recommends and executes the optimal security response based on the nature and urgency of the detected anomaly. For example, a minor anomaly automatically suggests a step-by-step response, such as simply strengthening surveillance camera monitoring and recording; a moderate anomaly automatically notifies nearby security guards on patrol and requests on-site inspection; and a serious anomaly automatically dispatches an emergency response while preparing to notify the police. To optimize the response, an algorithm is used that comprehensively considers factors such as the type of anomaly, past response experience in similar incidents, facility characteristics (e.g., layout, severity classification), and available security resources (e.g., location of the nearest security guard). Furthermore, automatic response priority determination enables efficient response to high-risk incidents, even when multiple anomalies occur simultaneously. An application interface is also implemented for security guards' mobile devices, providing navigation to the shortest route to the scene, real-time location information for objects requiring attention, and response procedure guidance. Fifth, the system provides evidence preservation and reporting functionality, automatically recording and saving the entire process of an abnormality incident, from its occurrence to its resolution, as evidence. Recorded content includes the original detection data, analysis results, report details, each step of the security response, and the resolution results. A time-stamped digital signature and audit trail are attached to prevent tampering. This data can be used as evidence in post-incident analysis and legal proceedings. After the incident is resolved, a detailed report is automatically generated and provided to both the facility manager and the security company. The report includes an overview of the incident, a chronological history, responses, and improvement proposals, and can be output in multiple formats, including PDF and HTML. These evidence and reports can also be used as a data source for the improvement cycle, contributing to the prevention of similar incidents and the optimization of responses. One key point to keep in mind in the technical implementation is ensuring interoperability between this system and security company systems. In particular, a software architecture employing an abstraction layer and adapter pattern will be implemented to accommodate each security company's unique systems and protocols. This will allow for flexible integration with new security companies and changes to existing integration specifications. To ensure redundancy and fault tolerance, multiple communication paths and automatic failover functions will be implemented for connections with key security companies, eliminating single points of failure. Furthermore, regular connectivity tests and heartbeat checks will monitor the constant availability of the integration function. These technical measures will enable reliable integration with security companies even in emergencies, significantly improving the security level of the facility.

[0089] A monitoring system comprising: means for acquiring data from a monitored area; analysis means for detecting targets from the data and identifying reappearance of the same target; and determination means for determining abnormal behavior based on the appearance information by either determining whether an index showing the behavioral characteristics of the same target exceeds a reference value, detecting an abnormal behavior pattern, or determining by AI.

[0090] A monitoring system comprising: means for acquiring data from a monitored area; analysis means for detecting targets from the data and identifying reappearance of the same target; and determination means for determining abnormal behavior based on the appearance information by either determining whether an index showing the behavioral characteristics of the same target exceeds a reference value, detecting an abnormal behavior pattern, or determining by AI. 2. The monitoring system according to claim 1, wherein the acquiring means includes at least one of a visible light camera, an infrared camera, an acoustic sensor, a biosensor, and a wireless signal detection sensor.

[0091] A surveillance system comprising: means for acquiring data from a monitored area; analysis means for detecting an object from the data and identifying the reappearance of the same object; and determination means for determining abnormal behavior based on the appearance information by either determining whether an index showing the behavioral characteristics of the same object exceeds a reference value, detecting an abnormal behavior pattern, or making a determination using AI, wherein the analysis means identifies the same object using at least one of image recognition, voice recognition, biometric authentication, wireless signal analysis, and behavior pattern analysis.

[0092] A monitoring system comprising: means for acquiring data from a monitored area; analysis means for detecting an object from the data and identifying the reappearance of the same object; and determination means for determining abnormal behavior based on the appearance information by either determining whether an index showing the behavioral characteristics of the same object exceeds a reference value, detecting an abnormal behavior pattern, or making a determination using AI, wherein the monitoring system further comprises execution means for executing at least one of the following actions based on the determination result by the determination means: issuing a warning, strengthening monitoring, coordinating with an external agency, and recording evidence.

[0093] A monitoring system comprising: a means for acquiring data from a monitored area; an analysis means for detecting an object from the data and identifying the reappearance of the same object; and a determination means for determining abnormal behavior based on the appearance information by either determining whether an index showing the behavioral characteristics of the same object exceeds a reference value, detecting an abnormal behavior pattern, or determining by AI, wherein the appearance information includes at least one of appearance frequency, length of stay, visit interval, movement route, and order of behavior.

[0094] A monitoring system comprising: means for acquiring data from a monitored area, analysis means for detecting an object from the data and identifying the reappearance of the same object, and determination means for determining abnormal behavior based on the appearance information by either determining whether an index showing the behavioral characteristics of the same object exceeds a reference value, detecting an abnormal behavior pattern, or making a determination using AI, wherein the monitoring system further comprises group analysis means for analyzing the relationships between multiple objects and detecting abnormal group behavior.

[0095] A monitoring system comprising: means for acquiring data from a monitored area, analysis means for detecting an object from the data and identifying the reappearance of the same object, and determination means for determining abnormal behavior based on the appearance information by either determining whether an index showing the behavioral characteristics of the same object exceeds a reference value, detecting an abnormal behavior pattern, or making a determination by AI, wherein the monitoring system further comprises learning means for automatically optimizing the reference value, abnormal behavior pattern, or AI determination criteria based on a comparison between the determination result by the determination means and the actual event result.

[0096] A monitoring system comprising: means for acquiring data from a monitored area; analysis means for detecting an object from the data and identifying the reappearance of the same object; and determination means for determining abnormal behavior based on the appearance information by either determining whether an index showing the behavioral characteristics of the same object exceeds a reference value, detecting an abnormal behavior pattern, or determining by AI, wherein the monitoring system further comprises prediction means for predicting future behavior based on the appearance information and environmental conditions, and proposing preventive measures for the predicted abnormal behavior.

[0097] A monitoring system comprising: means for acquiring data from a monitored area, analysis means for detecting an object from the data and identifying the reappearance of the same object, and determination means for determining abnormal behavior based on the appearance information by either determining whether an index showing the behavioral characteristics of the same object exceeds a reference value, detecting an abnormal behavior pattern, or determining by AI, further comprising output means for outputting the abnormal behavior determination result made by the determination means to an external system, wherein the output means is linked to a security system, a facility management system, or an emergency notification system via a communication network.

[0098] A monitoring system comprising: means for acquiring data from a monitored area; analysis means for detecting targets from the data and identifying reappearance of the same target; and determination means for determining abnormal behavior based on the appearance information by either determining whether an index showing the behavioral characteristics of the same target exceeds a reference value, detecting an abnormal behavior pattern, or determining by AI. wherein the output means outputs structured data including the type of abnormal behavior, the location of occurrence, the time of occurrence, target information, and the level of abnormality, and converts the data into a data format compatible with the external system before transmitting it.

[0099] In at least one embodiment, the system of the present invention provides comprehensive person of interest management capabilities for large-scale entertainment facilities such as amusement parks, theme parks, zoos, aquariums, museums, etc. These facilities must address a variety of person of interest, including nuisance perpetrators, complainers, voyeurs, stalkers, violent perpetrators, drunk visitors, past troublemakers, unauthorized photographers, vandals, and unauthorized entrants. The technical implementation of the system provides advanced surveillance capabilities that are adapted to the facility's specific environmental conditions and operational requirements. Integrating with the park's admission management system, the system works in conjunction with the ticket sales system, entrance gate, facial recognition system, RFID / IC card reader, etc. to verify the identity of all visitors upon entry. The facial recognition system is equipped with functions to detect mask wear, accommodate sunglasses wear, accommodate makeup and disguises, accommodate age changes, and accommodate changes in facial expression, achieving highly accurate identification under a variety of appearance changes. Specific technical specifications include a detection accuracy of over 95% (frontal face, under good lighting conditions), a processing speed of over 30 frames per second, and the ability to process up to 50 people simultaneously. Even when wearing a mask, the system maintains an identification accuracy of over 85% by analyzing the characteristics of the eyes and forehead, and when wearing sunglasses, the reduction in identification accuracy is kept to within 15% by combining it with infrared images. The detailed design of the park's surveillance system involves a network of surveillance cameras placed in each area (around attractions, restaurant areas, shopping areas, rest areas, around restrooms, parking lots, employee areas, etc.) to continuously track the movements of suspicious individuals within the park. The camera density will be at least 0.5 cameras per square meter in high-risk areas (cash handling counters, VIP areas, employee-only areas, etc.) and at least 0.2 cameras per square meter in general areas, with blind spots kept to 5% or less. The camera specifications will include 4K resolution, 30x optical zoom, infrared night vision, IP67-grade water and dust resistance, and resistance to wind speeds of 40 m / s. The technical implementation of person re-identification technology employs deep learning-based technology to ensure robustness against clothing changes, lighting changes, viewpoint changes, partial occlusion, etc. Specifically, a learning method combining Triplet Loss, Center Loss, and Focal Loss is applied to a ResNet-50-based feature extraction network to perform similarity calculations in a 512-dimensional feature vector space. The accuracy of person handover between cameras is over 95% in overlapping view areas and over 85% in non-overlapping areas. This makes it possible to understand the movement paths of target people throughout the park and predict their behavior. A detailed function of the behavior analysis system is to automatically distinguish between normal and abnormal park visitor behavior patterns. Normal patterns include common behaviors such as waiting for attractions, eating / resting, taking photos, shopping, and traveling. Abnormal patterns include staying in the same area for a long time (more than 60 minutes), unnatural wandering behavior (making three or more trips along the same route), approaching restricted areas, persistently approaching other visitors, photography behavior that suggests surreptitious photography (upward-facing angles, long-distance photography, etc.), and threatening behavior toward staff. The judgment algorithm employs a hybrid method combining a Hidden Markov Model, a Long Short-Term Memory Network, and a Graph Convolutional Network to achieve detailed analysis of time-series behavior patterns.

[0100] In at least one embodiment, the system of the present invention provides suspicious person detection and blacklist management functions for business events such as exhibitions, trade shows, business meetings, academic conferences, and conferences. At these events, vigilance against industrial espionage, competitor fraud investigators, attendees with a history of trouble, unauthorized photographers, security infringers, trade secret thieves, users of counterfeit IDs, and fraudulent information gatherers is required. The system implements specialized monitoring functions with the primary objectives of protecting intellectual property, preventing leaks of confidential information, and ensuring the safety of attendees. Through advanced integration with the pre-registration system, it checks against multiple data sources, including the attendee pre-registration database, guest list, exhibitor list, staff list, and VIP list, to detect intrusions by unauthorized attendees. The matching process applies multiple authentication methods, including facial recognition (accuracy of over 97%), ID OCR matching (accuracy of over 99%), QR code (registered trademark) / barcode authentication, and RFID / NFC tag authentication. For detecting counterfeit IDs, it automates physical feature inspections such as hologram verification, UV fluorescent reaction confirmation, micro-character recognition, and paper texture analysis. It achieves a detection rate of over 98% for illegally copied or altered IDs. The detailed implementation of the access control system will implement hierarchical access permission management for each area, including the exhibition area, business negotiation area, VIP lounge, press conference hall, and backstage area. Permission levels will be divided into six levels: general attendees, exhibitors, media personnel, VIPs, operational staff, and security staff, with accessible areas dynamically controlled according to each level. Detecting intrusions into unauthorized areas will combine physical boundary settings, virtual fence functions, and abnormal movement detection through movement analysis. Detection accuracy will be maintained at over 95%, with a false positive rate of less than 3%. As a confidential information protection system, it will be equipped with a function to detect abnormal approaches to exhibits. It will instantly detect inappropriate approaches to expensive exhibits, exhibits containing confidential information, exhibits containing hazardous materials, technical documents, prototypes, etc., and will notify security guards and issue an automatic voice warning. Approach detection will be carried out by combining laser distance sensors, infrared sensors, pressure sensors, vibration sensors, etc., and will detect approaches within a radius of 1m from the exhibit. It will achieve a detection accuracy of over 99% and a response time of less than 0.5 seconds. As an advanced function for detecting unauthorized photography, the system implements features such as smartphone and camera photography gesture recognition, flash light detection, and photography posture analysis. Gesture analysis technology detects photography-related actions such as camera positioning, shutter operation, and zoom operation with an accuracy of over 95%. In addition, acoustic analysis detects photography-related sounds such as shutter sounds, electronic sounds, and operation sounds, and also supports silent photography. When detection occurs, the system immediately issues a warning to the person in question and automatically requests them to stop taking photos.

[0101] In at least one embodiment, the system of the present invention provides comprehensive management functions for chronic complainers, suspicious customers, habitual payment refusals, verbally or physically abusive employees, those who make unreasonable demands, and those who intimidate and disrupt business operations in retail stores, restaurants, service industries, financial institutions, public facilities, etc. Dealing with these customers is an important issue from the perspectives of reducing the mental burden on employees, improving work efficiency, preventing negative impacts on other customers, and mitigating legal risks. The system realizes a comprehensive solution that integrates preventive response, gradual escalation, evidence preservation, and legal response support. The detailed technical implementation of the voice analysis system comprehensively analyzes the customer's speech volume, tone of voice, speech rate, emotional state, vocabulary choice, etc. to detect early signs of a complaint. The voice analysis engine uses a deep learning-based emotion recognition model (LSTM + Attention mechanism) to detect negative emotions such as anger, irritation, excitement, and intimidation with an accuracy of over 90%. Acoustic features such as fundamental frequency, formant, spectral centroid, and MFCC (Mel-frequency cepstral coefficients) are extracted, and emotion classification is performed in a 512-dimensional feature vector space. Real-time processing enables emotional state determination within three seconds of the start of speech. The video analytics system analyzes non-verbal information by comprehensively analyzing gestures, facial expressions, posture, gaze direction, movement patterns, etc. to estimate the customer's psychological state and behavioral intentions. Facial expression analysis uses muscle movement unit detection based on the Facial Action Coding System (FACS) to detect hidden emotions from subtle changes in facial expressions. Posture analysis uses human posture estimation technology such as OpenPose to automatically distinguish between threatening postures, postures preparing to attack, and postures preparing to flee. An integrated risk score is calculated to quantify the possibility of trouble escalation using a number from 0 to 100. As an advanced integration function with the past history database, detailed information such as customers' past complaint history, resolution methods, response personnel, type of problem, escalation process, and whether or not legal action was taken is structured and managed. The database structure is designed with tables such as customer basic information table, complaint history table, response history table, related party table, and legal action table, enabling efficient management of complex relational data. The similar case search function automatically recommends optimal response measures based on past success cases, supporting response standardization and quality improvement. Response pattern analysis using machine learning predicts the most effective response method for individual customers. As an automated evidence preservation system, it automatically saves audio recordings, video recordings, response records, chronological logs, etc. in a format that can be used as legal evidence. Digital signatures are added to evidence data to prevent tampering, ensuring a chain of custody. In addition, to protect privacy, automatic anonymization is applied to audio and video of non-related parties.

[0102] In at least one embodiment, the system of the present invention provides comprehensive crime prevention capabilities against shoplifters, purse snatchers, pickpockets, fraudsters, nuisance actors, suspicious individuals, theft gangs, organized criminals, etc. in shopping malls, department stores, large commercial facilities, outlet malls, underground shopping malls, etc. These facilities face limitations in conventional human surveillance due to their diverse storefronts, large numbers of customers, complex structures, diverse merchandise, cash handling, etc. The system provides a comprehensive crime prevention solution that integrates preventive surveillance, real-time response, evidence preservation, and damage minimization. In the detailed implementation of the repeat shoplifter detection system, high-speed matching is performed against a database of people who have been arrested for shoplifting in the past, people with a history of suspicious behavior, and people registered as suspicious at other stores. The database stores structured data such as facial image features, behavioral patterns, characteristics of the method used, items stolen, and crime time trends, and detection accuracy is improved through complex matching. Distributed parallel processing is used for the matching process, enabling searches of a database of one million items in less than one second. As an in-store behavior analysis system, it automatically detects shoplifting warning signs, such as abnormal patterns of approaching merchandise, prolonged browsing, wary behavior toward surrounding areas, unnatural movement patterns, and hand movements toward bags and pockets. The behavior analysis applies spatiotemporal behavior recognition technology (3D CNN, LSTM, Transformer) to understand complex behavioral sequences. Detectable behaviors include abnormally long periods of time holding merchandise (more than three times longer than normal), moving long distances while holding merchandise, moving to an exit that bypasses the cash register, and avoiding the gaze of store staff, and calculates a reliability score for each behavior. A shoplifting risk score (0-100) is calculated based on the integrated assessment, and an alert is issued when the threshold (default 80) is exceeded. As a function for detecting organized shoplifting groups, it detects crime patterns involving multiple people sharing roles. It comprehensively analyzes the division of roles (such as lookout, perpetrator, and transporter), coordinated behavior over time and space, and the use of communication methods (eye contact, gestures, and electronic devices). Group behavior analysis combines multi-agent behavior recognition technology with graph neural networks to learn the interaction patterns of multiple people. It also detects behavior patterns such as visiting multiple stores in a short period of time, enabling early detection of planned crimes. The technical implementation of the pickpocketing and baggage detection system detects suspicious approaches through detailed analysis of a person's approach pattern, hand movements, gaze direction, body orientation, and movement speed. In particular, surveillance is focused on areas with high crime rates, such as rest areas, areas around ATMs, escalators, elevators, and restaurants. The detection algorithm combines distance calculations between people, approach speed analysis, hand movement tracking, and gaze direction estimation to distinguish between normal and suspicious approaches. Upon detection, the system wirelessly notifies nearby security guards, indirectly warns the target (via public address system, signboards, etc.), and automatically records video evidence.

[0103] In at least one embodiment, the system of the present invention provides management functions for suspected patients, persons with problematic behavior, suspicious visitors, drug abusers, medical equipment thieves, information leakers, etc. in medical facilities such as hospitals, clinics, nursing homes, pharmacies, and testing centers. Medical facilities have special requirements, such as protecting patient privacy and ensuring safety, preventing verbal abuse and violence against medical staff, preventing the illegal acquisition of drugs, preventing the theft of medical equipment, and preventing hospital-acquired infections. The system supports the realization of a safe and efficient medical environment while complying with medical ethics and legal requirements. In the detailed implementation of the system for early identification of abusive and violent patients, it will detect in advance the arrival of patients who have previously engaged in problematic behavior such as verbal abuse, violence, sexual harassment, threatening behavior toward medical staff, or property damage, and provide information to the attending physician or nurse. The patient database will be structured and managed to include information such as the type of problematic behavior, the date and time of occurrence, response records, mental state evaluation, medication information, and family composition, enabling comprehensive risk assessment. When such behavior is detected, it will automatically suggest preventative measures such as prioritizing the allocation of experienced staff, preparing a private room for response, having security staff on standby, and preparing sedatives. As an emotional state analysis system, it analyzes the patient's emotional state from facial expressions, tone of voice, physical movements, walking patterns, etc., to detect early signs of trouble. Facial expression analysis uses a dedicated model that takes into account factors unique to the medical environment (wearing a mask, changes in appearance due to illness, effects of medication, etc.). Voice analysis identifies medical-related emotional states such as pain, anxiety, anger, and confusion with a high degree of accuracy. For patients in a mentally unstable state, it automatically suggests responses such as allocating appropriate medical staff, adjusting the environment (lighting, sound, etc.), and contacting family members. As an advanced implementation of the drug illicit acquisition prevention system, it detects "doctor shopping," the act of visiting multiple medical institutions in a short period of time, and prevents the illegal acquisition of narcotics, psychotropic drugs, and prescription drugs. Secure data sharing between local medical institutions allows for comprehensive analysis of the same patient's medical history, the type and amount of prescribed medication, the interval between visits, and the consistency of symptoms, automatically detecting abnormal patterns. Particularly strict monitoring is carried out for drugs regulated by the Pharmaceutical Affairs Law, confirming prescription limits, preventing duplicate prescriptions, and assessing the possibility of illegal resale. As a function to help prevent infection within hospitals, it tracks the behavior of infectious disease patients, identifies close contacts, analyzes movement patterns within the hospital, and helps prevent the spread of infection. It records the movement history of patients and staff in detail, enabling the rapid identification of contacts when an infection is discovered. It also monitors the implementation of hand disinfection, mask wearing, and social distancing, supporting thorough infection prevention measures.

[0104] In at least one embodiment, the system of the present invention provides safety management functions such as preventing intrusion by suspicious individuals, managing students with problematic behavior, dealing with parental issues, preventing bullying, and ensuring the safety of faculty and staff in educational facilities such as schools, universities, vocational schools, cram schools, preparatory schools, kindergartens, and daycare centers.Educational facilities have special requirements such as protecting minors, maintaining an educational environment, preventing intrusion by unauthorized persons, responding to emergencies, and protecting privacy.The system realizes advanced monitoring functions that balance educational considerations with ensuring safety. In the detailed implementation of the suspicious person intrusion detection system, the system checks against a pre-registered database of school personnel (teachers, staff, students, parents, contractors, etc.) to instantly detect intrusions by outsiders. The registered data includes facial images, fingerprints, IC card information, vehicle license plates, and school visit schedules, enabling highly accurate identification through multiple authentication. Intruders are automatically dealt with in stages, including confirming the purpose of their visit via the intercom, providing instructions for procedures at reception, and being handled by security guards. The system also checks against a database of suspect individuals who have previously caused trouble at school, behaved suspiciously in the neighborhood, or been involved in incidents of accosting children, enabling early detection of high-risk intruders. As a bullying and violence detection system, it automatically detects violent acts between students, bullying, vandalism, blackmail, etc. through video analysis within the school. Behavioral analysis involves a comprehensive evaluation of the distance between people, contact patterns, posture, facial expressions, crowd formation, etc. Audio analysis technology also detects abnormal sounds such as abusive language, threats, crying, and cries for help, helping to detect bullying in hard-to-see places. To protect privacy, detected data is strictly managed and its use is limited to educational purposes. The emergency response system will be equipped with automatic response functions in the event of an emergency, such as an intrusion by a suspicious person, a violent incident, a natural disaster, or a fire. Depending on the type of emergency, it will automatically lock classrooms, broadcast evacuation guidance, make emergency calls, contact parents, etc. In addition, a student safety confirmation system will quickly confirm the completion of evacuation, identify missing persons, and grasp the injured, thereby minimizing damage. To combat parental issues, the system will implement a support function for dealing with parents who exhibit problematic behavior such as excessive demands, abusive language, threatening behavior, etc. By recording conversations and actions in interview rooms, etc., the system will help preserve evidence of the issues and consider appropriate countermeasures.

[0105] In at least one embodiment, the system of the present invention provides detection and countermeasure functions for financial institutions such as banks, credit unions, securities companies, insurance companies, and consumer finance companies against special fraud receivers, ATM fraudsters, robbers, suspicious visitors, users of forged documents, and money launderers. Financial institutions have special responsibilities such as handling large amounts of cash, managing personal information, protecting the elderly, and complying with legal regulations. The system provides a comprehensive security solution that integrates financial crime prevention, customer protection, and asset preservation. In an advanced implementation of a special fraud detection system, the system performs detailed behavioral analysis of accompanying persons when elderly customers withdraw large amounts of cash. Behavioral analysis items include giving instructions to customers, monitoring the customer's facial expressions and reactions, frequent cell phone calls, signs of nervousness, and unnatural waiting behavior, and implements a detection algorithm for each item. The system detects accompanying persons' behavioral patterns when giving instructions to customers (gestures, whispering, showing the cell phone screen, etc.) with over 90% accuracy, and detects situations in which the customer shows anxious expressions (tense facial muscles, unsteady gaze, stiff body, etc.) with over 85% accuracy. A fraud likelihood score (0-100) is calculated based on the integrated assessment, and a warning is issued to the teller if the score is high. Detailed functions of the ATM-area suspicious activity detection system include detecting abnormal approaches to customers while they are operating an ATM, the installation of skimming devices, attempts to peek at PIN numbers, card exchange fraud, and forced cash withdrawals. The technology implementation combines distance measurement between people (with an accuracy of within ±10cm), gaze direction estimation (with an angle accuracy of within ±5 degrees), hand movement analysis, and foreign object detection (metal detection, image analysis). It also monitors actions such as people lingering around ATMs for long periods of time late at night, multiple people occupying an ATM, and waiting in a vehicle, to detect early signs of a robbery. When a suspicious activity is detected, it automatically takes a step-by-step approach, including dispatching security guards, notifying the police, making announcements to customers, and temporarily suspending the ATM. As a counterfeit document and ID detection system, it automatically determines the authenticity of documents such as ID cards, seal certificates, and income certificates presented by customers. Detection technologies include checking anti-counterfeit printing using ultraviolet light, detecting hidden characters using infrared photography, micro character recognition, paper texture analysis, and print quality evaluation. Character recognition using OCR technology and comparison with known legitimate formats detects layout anomalies, font anomalies, and content anomalies. It achieves a counterfeit detection accuracy of over 95%, automatically extracting cases requiring detailed inspection. As a money laundering detection system, it comprehensively analyzes customer transaction patterns, frequency of visits, transaction amounts, related parties, etc. to assess the possibility of money laundering. It automatically detects suspicious activity such as large amounts of cash transactions, opening a large number of accounts in a short period of time, related transactions under multiple names, and frequent overseas remittances, and supports reporting to the Financial Services Agency and other authorities.

[0106] In at least one embodiment, the system of the present invention provides the capability to detect crimes and terrorist acts, such as molestation, voyeurism, pickpocketing, the smuggling of explosives, the abandonment of suspicious luggage, terrorists, smugglers, and illegal immigrants, at transportation facilities such as train stations, airports, bus terminals, ports, and highway rest areas. Transportation facilities face special environmental conditions, such as large numbers of users, short transfer times, congestion, international nature, and high-speed travel. The system realizes a comprehensive security system that integrates public transportation safety, counter-terrorism measures, and crime prevention. The highly accurate implementation of the molestation detection system uses video analysis to detect inappropriate physical contact on crowded trains, escalators, stairs, platforms, etc. The technical implementation comprehensively measures the distance between people, identifies contact points, measures contact time, analyzes the victim's reaction, and analyzes the perpetrator's behavioral patterns. Deep learning-based posture estimation technology (OpenPose, etc.) is used to accurately track the position and movement of hands from the joint coordinates of the human body. Analysis of hand and finger movements distinguishes between natural and intentional contact, detecting molestation with an accuracy of over 95%. The victim's facial expressions and reactions (confusion, disgust, avoidance, etc.) are also simultaneously analyzed to calculate an overall molestation likelihood score. By comparing the face with a database of habitual molesters, it is possible to quickly detect suspicious individuals. When a person is detected, the system will issue a warning over the train announcements (using indirect language such as "Please refrain from causing trouble to other passengers..."), notify station staff and security guards, automatically save video evidence, and prepare for police officers to wait at the next station. The detailed functions of the suspicious baggage detection system include automatically detecting baggage that has been left unattended for more than a certain period of time, baggage with an unknown owner, baggage of an abnormal shape or size, baggage that gives a strong reaction to metal detectors, and baggage that shows a reaction to chemical detectors. The technical implementation uses object tracking technology to continuously monitor the relationship between baggage and people, and determines that baggage is abandoned if it is separated from the person for more than five minutes. For abandoned baggage, attempts are made to identify the owner by analyzing its relationship with surrounding people, and if the owner cannot be found, measures are taken to consider the possibility that it may be an explosive. Shape analysis detects unusual shapes (cylindrical, spherical, wire-like, etc.) that differ from normal baggage, and a detailed analysis of dangerous items is performed by linking with existing security equipment such as X-ray machines, metal detectors, and chemical detectors. As a crowd behavior analysis system, it detects crowd flow, density, abnormal gatherings, panic, signs of riots, etc. in train stations, airports, etc. Crowd density is square meters It comprehensively monitors overcrowding exceeding six people per square meter, sudden changes in speed, abnormal changes in direction by the crowd, and the occurrence of shouts and screams, and can detect early signs of crowd accidents or terrorist incidents. As a countermeasure against international terrorism, the system will be equipped with a function for matching with databases of internationally wanted persons, terrorist watch lists, and illegal immigrants, and will strengthen border control measures at international transportation hubs such as airports and ports. For multinational response, the system will also be equipped with a function for sharing information with law enforcement agencies in each country.

[0107] Mode for carrying out the invention (Comprehensive management system for banned customers in pachinko parlors and casinos) In at least one embodiment, the system of the present invention provides highly accurate detection and management of banned customers at gambling facilities such as pachinko parlors, slot machine parlors, casinos, horse racing tracks, bicycle racing tracks, and boat racing tracks. These facilities require a variety of measures to address banned customers, such as restricting entry based on self-reported gambling addiction, excluding members of organized crime groups and anti-social forces, detecting fraudsters (cheaters, affiliated fraudsters, etc.), managing violent and intimidating users, identifying those who refuse to pay, and preventing minors from entering. The system provides a comprehensive ban management solution that integrates legal compliance, social responsibility, and management risk mitigation. In an advanced implementation of the real-time entry matching system, multiple high-resolution cameras (4K or higher, processing at 60 frames per second) installed at facility entrances instantly perform facial recognition of all visitors. A database of banned individuals is categorized and managed to include self-reported entry restrictions, members of organized crime groups, fraudsters, violent actors, payment refusals, verbal abuse towards employees, property damage, and business disruption, achieving high-speed searches (within one second) for a total of 100,000 registered individuals. Facial recognition accuracy is over 98% for frontal faces, over 95% for oblique faces (+ / - 45 degrees), and over 90% when wearing a mask. As a multi-angle facial image registration system, it comprehensively manages facial images of banned individuals from multiple angles (front, 45-degree diagonal views to the left and right, side views to the left and right, bird's-eye view, elevation), with multiple expressions (neutral, smiling, angry, troubled), at multiple points in time (annual images from the past five years), and under multiple lighting conditions (natural light, fluorescent light, LED lighting, backlighting).Registration methods support a variety of input methods, including dedicated photography with a high-resolution digital camera (20 million pixels or more), simple photography with a smartphone (5 million pixels or more), automatic clipping from existing surveillance camera footage, facial image extraction from identification cards, importing images provided by the police, and assistance with employee portrait creation. As a detailed function of the gambling addiction prevention system, it will implement integration with a self-exclusion program based on self-reporting by the individual and their family. The self-exclusion registrant database will manage information such as the period of restriction (1 month, 3 months, 6 months, 1 year, indefinite), reason for restriction (addiction treatment, household management, self-control, etc.), emergency contact information (family, medical institution, counseling agency), and medical institution linkage information. When a gambling addiction is detected, entry will be blocked and appropriate guidance (referral to a treatment institution, information on a counseling center, etc.) will be automatically provided according to the reason for registration. In addition, a family notification function will provide comprehensive support in cooperation with medical institutions and families that support the recovery of addicted patients. As an advanced implementation of a fraud detection system, it automatically detects fraudulent patterns such as cheating (obtaining winnings through fraudulent means), coordinated fraud, tampering with machines, and collusion with employees. By analyzing gaming behavior, it comprehensively monitors abnormal win rates, unnatural machine selection patterns, frequency of contact with specific employees, unusual contact with machines, and the use of brought-in devices. By learning fraudulent patterns through machine learning, it builds an adaptive detection system that can also handle new types of fraudulent methods. When a fraud is detected, it takes a series of steps, including stopping play on the relevant gaming machine, having security personnel check, preserving video evidence, and preparing to report to the police. The system will be equipped with a function to check against a database of organized crime members provided by the Public Safety Commission, a database of anti-social organizations from related companies, and a database of people involved in past violent incidents. To improve detection accuracy, it will also use supplementary identification methods such as tattoo recognition, distinctive clothing pattern recognition, and accompanying person pattern analysis. When a crime is detected, it will automatically take measures such as immediately denying entry, reporting to the police, sharing information with other stores, and preparing for legal action. As a technical implementation of the inter-store information sharing system, it provides a function for securely sharing ban information between stores in the same chain, between stores in a region, and between industry groups. Shared data includes the reason for the ban, the period, the importance, related incidents, etc., and is appropriately anonymized to protect privacy. API-based information exchange enables real-time information updates and comparisons, and automatically notifies the original store when a banned person is found at another store. As a gaming behavior monitoring system, it monitors long periods of play, large investments, abnormal win rates, unnatural playing patterns, etc., to detect signs of gambling addiction and possible fraudulent behavior. It integrates and analyzes gaming data (investment amount, time, wins and losses, machine movement, etc.) and video data to detect problematic gaming behavior early. For those in question, it implements gradual interventions such as inviting them to take a break, referring them to a consultation service, and suggesting that they limit their playing time.

[0108] MODE FOR CARRYING OUT THE INVENTION (Comprehensive Security and Compliance Management System for Casinos) In at least one embodiment, the system of the present invention provides comprehensive security and compliance management functions for casinos within integrated resorts (IRs), shipboard casinos, overseas casinos, etc. Casinos have advanced and complex requirements, such as anti-money laundering measures, VIP customer management, measures against professional gamblers, counting detection, collusion detection, dealer monitoring, chip theft prevention, minor exclusion, and self-exclusion program management. The system provides a world-class solution that complies with international casino regulatory standards, AML (Anti-Money Laundering) regulations, KYC (Know Your Customer) requirements, etc. As a detailed implementation of the VIP customer and high roller management system, it comprehensively performs identity verification of high-value transaction customers, source of funds verification, transaction history analysis, risk assessment, etc. The customer database will structure and manage information such as ID card information, passport information, asset verification, occupation and income information, past transaction history, and activity history at other casinos. A facial recognition system will automatically identify customers upon entry and compare them with past transaction patterns to detect abnormal fund movements or changes in transaction size. An automatic generation function for Suspicious Activity Reports (SARs) in accordance with FATF (Financial Action Task Force) guidelines will automatically detect and report transactions suspected of money laundering. This highly accurate implementation of a card counting detection system automatically detects fraudulent card memorization and calculation behavior in card games such as blackjack. It comprehensively monitors players' gaze tracking, patterns of betting amount fluctuations, analysis of decision-making time, and finger movements (finger movements that appear to count cards). By learning normal playing patterns using machine learning, it automatically identifies players who exhibit statistically abnormal winning rates and decision patterns. For players suspected of counting, it implements incremental countermeasures such as increasing shuffling frequency, adjusting bet limits, and proposing game changes. As a dealer and employee monitoring system, it detects dealer misconduct (intentional mishandling, chip manipulation, collusion with customers, etc.), cashier misconduct (embezzlement of cash and chips, etc.), and security staff misconduct. It detects statistical anomalies through detailed analysis of dealer hand movements, dealing patterns, eye contact with customers, and abnormal win / loss distribution. It also uses audio analysis to detect suspicious conversations and coded communications between dealers and customers. Early detection of internal fraud maintains the reliability of the casino and prevents business losses. Detailed implementation of the chip and cash management system tracks and manages the entire lifecycle of chips, from manufacturing to disposal. High-value chips with built-in RFID chips enable location tracking of individual chips, transaction history recording, and counterfeit chip detection. The chip counting system monitors the total amount of chips on the table in real time and immediately detects loss or theft. In the cash handling area, it works in conjunction with banknote counters, metal detectors, X-ray inspection equipment, etc. to provide functions such as detecting counterfeit bills and discovering hidden cash. For integrated gaming table monitoring, multiple high-resolution cameras (overhead, side view, player view) are placed at each gaming table to record all transactions. All events, such as card dealing, chip movements, bet placement, and win / loss results, are automatically recognized and recorded for future audits and dispute resolution. AI image analysis automatically recognizes card types and numbers, chip types and numbers, player actions, etc., detecting even subtle fraudulent behavior that would be difficult to detect through human monitoring. As a system for complying with international regulations, it implements compliance management functions that comply with the casino regulatory requirements of each country (Nevada Gaming Control Board, Macau Gaming Inspection and Coordination Bureau, Singapore Casino Regulatory Authority, etc.) It streamlines compliance with complex international regulations with functions such as managing record retention periods in accordance with regulatory requirements, generating audit logs, and automatically creating reports to regulatory authorities.

[0109] Mode for carrying out the invention (Pachinko and casino store collaboration system) In at least one embodiment, the system of the present invention provides a secure and efficient sharing and collaboration function for ban information among multiple pachinko parlors and casino facilities. Information sharing at various levels, such as between chain parlors, franchises, local parlors, and industry associations, effectively monitors and restricts the activities of banned individuals at other parlors. The system improves the soundness of the industry as a whole while ensuring privacy protection, information security, and legal compliance. As a detailed implementation of the distributed database architecture, we will build a hybrid system that enables necessary information sharing while maintaining the independence of each store. Store-specific information (detailed behavioral records, internal circumstances, etc.) will be stored in a local database, and only information to be shared (basic ban information, importance, period, etc.) will be registered in a central database or distributed ledger. An event-driven architecture will be used for data synchronization, and additions, updates, and deletions of ban information will be distributed to other stores in real time. Advanced privacy protection features include differential privacy, k-anonymity, homomorphic encryption, and other cutting-edge technologies, enabling useful information sharing while minimizing the possibility of identifying individuals. Facial image features of banned individuals are one-way hashed and managed in a way that makes it impossible to restore the original image. Shared data is limited to the bare minimum, including the broad classification of the reason for the ban (violence, fraud, addiction prevention, etc.), risk level, and validity period, with more detailed information provided upon request. The blockchain-based information management system will implement a distributed ledger system aimed at preventing tampering with ban information, ensuring transparency, and maintaining audit trails. When each store registers or updates ban information, it is recorded in the ledger after reaching a consensus among multiple nodes, making subsequent tampering technically impossible. Smart contract functionality will enable automatic management of ban periods, permission-based access control, automatic expiration processing, and more. As a real-time matching and notification system, it will implement a function to immediately detect and notify when a person who has been banned from store A enters store B. When detected, it will automatically notify the store where the person was banned, display a warning at the store where the detection occurred, and share the person's behavioral history. The notification will include the time of detection, location of detection, reliability score, recommended response measures, etc., to support a prompt and appropriate response. As a system ensuring legal compliance, it implements functions to comply with legal requirements such as the Personal Information Protection Act, industry self-regulation, local ordinances, etc. It ensures reliable compliance with complex legal requirements through automatic management of data retention periods (automatic deletion after the legal period has elapsed), functions to respond to requests for disclosure by the individual, functions to respond to requests for deletion (right to be forgotten), and functions to provide information to auditing authorities.

[0110] Mode for carrying out the invention (AI-based system for responding to the evolution of fraudulent behavior patterns) In at least one embodiment, the system of the present invention provides an AI system equipped with continuous learning and adaptive learning functions to respond to the evolution of fraudulent methods in the pachinko and casino industry. This system uses machine learning to adaptively respond to new types of fraudulent methods, increasingly sophisticated fraud patterns, and the evolution of organized crime, which are difficult to deal with using conventional fixed fraud detection rules. As a detailed implementation of the fraudulent behavior pattern learning system, it learns behavioral patterns, time series changes, interactions between parties, etc. from a database of past fraud cases, and builds an automatic detection model for similar patterns. The learning data includes video data of cheating, time series behavior data leading up to the discovery of fraud, collaboration patterns among multiple people, detailed records of the equipment and methods used, etc. Deep learning models (LSTM, GRU, Transformer, etc.) simultaneously learn complex time series patterns and spatial relationships, making it possible to detect subtle signs of fraud that were previously difficult to detect. By applying transfer learning and few-shot learning, patterns can be efficiently learned even from a small number of new fraud cases. Information on new fraud methods that have occurred in other stores or regions is used as learning data to update the detection model before they occur in your store. A meta-learning approach gives the system the ability to quickly learn new fraud methods, improving the speed at which it can adapt to unknown methods. By introducing adversarial training, we will improve robustness against detection evasion techniques used by fraudsters. We generate adversarial samples of behavioral patterns that fraudsters may use to deceive the detection system, and then perform training to enable accurate detection of these as well. We will use GANs (Generative Adversarial Networks) to generate hypothetical fraud scenarios that do not actually occur, expanding the training data and strengthening detection capabilities. Real-time adaptive learning will implement a function to continuously update the model from new data collected during business hours. Online learning algorithms will instantly reflect new normal and abnormal patterns in learning, enabling adaptation to environmental changes. To prevent catastrophic forgetting (the problem of forgetting past learning content), technologies such as Elastic Weight Consolidation and Progressive Neural Networks will be applied. As a collaborative learning system with human experts, it will implement a function that integrates the knowledge of veteran employees with AI learning. Minor anomalies and intuitive judgments discovered by experienced store staff will be used as learning data, synergistically combining human experiential knowledge with AI's computing power. Explainable AI technology will present the basis for AI decisions in a form that humans can understand, promoting mutual learning between humans and AI.

[0111] Mode for carrying out the invention (Entrance waiting line / entrance integrated monitoring system) In at least one embodiment, the system of the present invention provides a high-precision system that comprehensively monitors the entire process at the entrance of a facility such as a pachinko parlor or casino, from waiting in line to completing entry. It continuously performs early detection of suspicious individuals, behavioral pattern analysis, and risk assessment at multiple stages, such as the queue before opening, the ticket distribution area, the ID verification area, the baggage inspection area, and the entrance gate. The system detects threats to the facility before they enter, realizing an innovative security solution that enables preventative response. In the detailed implementation of the queue monitoring system, long-range surveillance cameras (50x optical zoom, 4K resolution, infrared night vision) are installed from the roads surrounding the facility to the entrance to monitor people's movements from several hours before opening. Queue formation pattern analysis detects predictive behavior such as unusual early morning arrivals, unnatural queue-keeping behavior, and multiple people reserving spots. A facial recognition system pre-identifies all people in line and compares them with a database of suspicious people. Detection accuracy is over 85% at a distance of 50m and over 95% at a distance of 20m, and even when wearing a mask, identification accuracy of over 80% is maintained by analyzing the characteristics of the eyes and forehead. As a behavioral psychology analysis system, it implements a function to estimate the psychological state and behavioral intentions of people waiting in line. It automatically detects signs of tension (frequent checking the clock, talking on a cell phone, restless movements, alert behavior to surroundings, etc.), signs of cooperative behavior (exchanging eye contact with multiple people, hand signals, maintaining positional relationships, etc.), and signs of concealing behavior (hiding the face, avoiding the camera, unnatural clothing, etc.). By learning normal waiting behavior patterns using machine learning, it can identify people who exhibit statistical abnormalities with over 90% accuracy. The technical implementation of the entrance multi-step authentication system involves detailed identity verification and behavioral analysis at each stage of the entry process. The first stage (outdoor waiting line) involves long-range facial recognition and database matching, the second stage (indoor reception) involves close-range high-precision facial recognition and ID verification, the third stage (baggage inspection) involves personal item inspection and X-ray analysis, and the fourth stage (entrance gate) involves final verification and entry recording. The detection information at each stage is combined to calculate an overall risk score (0-100).

[0112] Mode for carrying out the invention (Multiple camera integrated tracking and dynamic surveillance enhanced system) In at least one embodiment, the system of the present invention integrates and controls various types of security cameras (fixed cameras, PTZ cameras, dome cameras, concealed cameras, high-resolution cameras, infrared cameras, thermal cameras, etc.) installed within a facility, enabling continuous tracking of suspect individuals and dynamic surveillance enhancement. Collaborative control between cameras enables comprehensive surveillance without blind spots and adaptive allocation of surveillance resources according to threat levels. In the detailed implementation of the multi-camera integrated control system, all surveillance cameras within the facility (supporting a large-scale network of over 100 cameras) are managed from a central control system, enabling real-time coordinated operation. When a suspicious individual is detected, PTZ cameras around the individual's current location are automatically directed and switched to tracking mode, and fixed cameras along the individual's path are predictively activated. Camera control applies algorithms such as physical blind spot calculations, calculation of optimal shooting angles, and automatic adjustment of zoom magnification, ensuring the highest quality of footage at all times. As a dynamic surveillance enhancement system, it implements a function to automatically adjust the allocation of surveillance resources according to the detected threat level. For low risk (score 20-40), normal surveillance is maintained; for medium risk (score 40-70), the camera resolution and frame rate in the relevant area are increased; for high risk (score 70-90), simultaneous tracking with multiple cameras and maximum recording quality are applied; and for the highest risk (score 90-100), priority tracking mode for the relevant person is activated for all cameras in the building. By optimizing resource allocation, the system maintains overall surveillance quality while focusing on important monitoring targets. The technical implementation of the person trajectory prediction and proactive monitoring system includes a function to predict future destinations based on current movement patterns and prepare cameras at predicted locations in advance. A movement prediction model is built based on past people flow data, facility layout, and estimated destinations (game areas, prize exchange booths, restrooms, exits, etc.), achieving a destination prediction accuracy of over 80%. Advance preparations for filming (focus adjustment, exposure adjustment, PTZ preparatory operation, etc.) are instructed to cameras at predicted locations, and high-quality filming begins immediately upon the target person's arrival. As an inter-camera information fusion system, it implements a function that integrates video information from multiple cameras capturing the same object, enabling more detailed and accurate analysis. It integrates video from different angles in 3D to more accurately extract physical characteristics such as a p...

Claims

1. A surveillance method comprising: a means for storing or searching for a person; a detection means for detecting the face or physical features of a person from a captured video or image; a face matching means for matching or analyzing the detected face with face images stored in an image database using AI; and a means for recording the frequency and time intervals of appearance of the same person as time-series data and determining repeated appearance patterns using statistical anomaly detection.

2. 2. The surveillance method according to claim 1, further comprising a warning means for issuing a warning based on the result of the face matching by said face matching means, said warning means issuing a warning in stages according to a threat level.

3. The surveillance method according to claim 1, wherein the AI ​​performs facial recognition processing using deep learning and integrates images from multiple image capture devices to track the same person.

4. 2. The surveillance method according to claim 1, wherein the detection means detects a person from a part of the face even when the person is wearing a mask, and identifies the person by combining partial facial features with a movement pattern.

5. A surveillance method comprising: a means for storing or searching for a person; a detection means for detecting the face or physical features of a person from a captured video or image; said detection means having a partial recognition function for identifying a person from the visible part of the face by deep learning even when the person is wearing a mask or sunglasses; and a face matching means for matching or analyzing the detected face with face images stored in an image database by AI; wherein said image database stores facial images of prohibited persons, and when said prohibited person is detected, the frequency of appearance of the same person is recorded in chronological order.