101 results about "Surveillance data" patented technology

The invention relates to the technical field of video monitoring, and discloses a camera cooperative monitoring method and system based on multi-modal perception, and the method comprises the steps: detecting an abnormal event signal in an environment through a non-visual sensor node, triggering the activation of a camera, and carrying out the visual capture of a target region; the camera performs relay tracking based on target feature matching to generate a continuous motion track; performing multi-view collaborative shielding on privacy sensitive information in the tracking target, generating desensitized monitoring data, and uploading the desensitized monitoring data; and optimizing the energy consumption of the camera in a non-event triggering period, and updating the behavior recognition model based on the desensitization data. According to the method, the contradiction between privacy protection and monitoring efficiency is solved, cross-regional model evolution and energy consumption reduction are realized, and the problems of response lag, data redundancy, privacy disclosure and high energy consumption of traditional monitoring are systematically avoided.

The invention relates to the technical field of traffic management, in particular to a road traffic dispersion and emergency command system construction method based on multi-source data fusion, and the method comprises the steps: collecting traffic flow sensor data, video monitoring data, meteorological data, social media user feedback data and the like through a multi-source data collection module; a data fusion and processing module is used, and data-level, information-level and decision-level fusion algorithms are adopted to carry out standardization processing, key information extraction and comprehensive evaluation and prediction on multi-source data. And constructing a traffic flow prediction model based on machine learning, such as a long and short-term memory network model, and accurately predicting the traffic flow. And according to the prediction result and the real-time traffic condition, a traffic dispersion strategy making module is used for adjusting signal lamp timing, issuing traffic guidance information and allocating traffic police resources. In the aspect of emergency command, multi-source data are integrated to evaluate event severity, deploy rescue resources and determine an optimal rescue route, and multi-department information sharing and cooperative work are achieved.

The invention relates to the technical field of community video monitoring, and discloses a community personnel abnormal behavior video monitoring method based on human factor engineering, and the method comprises the steps: dividing community monitoring video data based on a preset activity region, and obtaining the monitoring data of each sub-region, including a personnel track, environment interaction and the like; determining the abnormal level of each sub-region according to the movement frequency of the personnel trajectory data and the interaction strength of the environment interaction data; key monitoring nodes are calculated in combination with abnormal levels and the like, and the scanning sequence and the scanning duration are adjusted and determined according to a dynamic path; then generating observation anchor points and redundant anchor points at the boundaries of the sub-regions; and finally, associating and integrating to generate a monitoring path. According to the method, through multi-dimensional data processing and dynamic optimization, the accuracy, flexibility and environmental adaptability of community abnormal behavior monitoring are improved, and the method is suitable for community safety management.

The invention relates to a chemical plant dangerous area electronic fence generation method and system, and the method comprises the steps: obtaining the positioning data of a target person, the positioning data of production equipment, and the video monitoring data and environment sensing data of a target area, carrying out the recognition based on the video monitoring data, and obtaining the behavior data of the target person, the method comprises the steps of dividing a target area into virtual grids based on a dynamic grid algorithm, determining distribution of geographic features based on GIS geographic information of the target area, filtering impassable areas based on the distribution of the geographic features, aggregating and combining adjacent grids, generating a closed boundary, and establishing a risk level evaluation model to evaluate the risk level of each virtual grid. The boundary adjusting module is used for adjusting the minimum buffer area width of the closed boundary to obtain a final electronic fence boundary, and the electronic fence boundary is used for carrying out risk early warning. According to the invention, dynamic and accurate sensing and protection of the high-risk area in the chemical plant are realized.

The invention relates to the technical field of video monitoring transmission, in particular to a video monitoring data rapid transmission method and system based on 5G, and the method comprises the steps: collecting a monitoring video in a single road direction, and determining each connected domain in each frame of monitoring video and a matched connected domain; obtaining a motion factor of each connected domain, and further obtaining a motion connected domain in each frame of monitoring video; constructing information richness of each frame of monitoring video; obtaining the direction disorder degree of each frame of monitoring video; obtaining the picture attention degree of each frame of monitoring video; determining a quantization compression parameter of the video per second; and optimizing a quantization parameter of the HEVC technology in real time based on the quantization compression parameter, and performing coding compression transmission on the monitoring video by using the optimized HEVC technology. Therefore, the transmission efficiency is maximized on the premise of ensuring the quality of the key video frame.

The invention discloses a method and a device for enhancing scene trajectory prediction by fusing video monitoring and ADS-B, which can improve the precision and reliability of airport scene situation awareness, further accurately predict the moving trajectory of airplanes and other vehicles, and provide powerful technical support for airport scene management and flight safety. The method comprises the following steps: (1) synchronizing video monitoring data and ADSB data by using a timestamp, and realizing time-space alignment of different modal data through coordinate conversion; (2) determining a corresponding relation among different data sources through a data association technology, and constructing positive and negative sample pairs; (3) pre-training the multi-source heterogeneous data by using a comparative learning algorithm to extract and fuse key features, and constructing an efficient situation information representation model; and (4) adopting a pre-trained model as the basis of trajectory feature extraction, and inputting the basis to a long short-term memory (LSTM) network to predict the trajectory of the airport scene.

The invention discloses a rail transit anomaly detection method and device based on a digital twin technology, and relates to the field of artificial intelligence, the field of big data or other related fields, and the detection method comprises the steps: collecting environment perception data and video monitoring data of at least one target road section, and obtaining a real-time data stream in a first target time period; analyzing the real-time data flow based on a preset abnormal behavior rule to obtain an abnormal analysis result; a digital twinborn model is called, the digital twinborn model is detected according to the abnormal analysis result and the real-time data flow, a detection result is obtained, and the digital twinborn model is used for synchronously reflecting the actual physical environment, the basic equipment layout and the equipment operation state in the rail transit scene; and generating an anomaly detection report based on the detection result and triggering an anomaly warning. The technical problem that the emergency response speed is low due to the fact that a traditional video monitoring system lacks intelligent means in on-orbit traffic elevated section safety supervision in the prior art is solved.

The invention discloses an intelligent campus student activity trajectory tracking method and system based on front and rear end fusion learning, and relates to the technical field of intelligent campus security. An embedded front end collects videos of a plurality of monitoring cameras of a campus, carries out target detection and extracts primary features of student targets; uploading the extracted primary features and the video to a back-end server; and the back-end server extracts advanced features of human body postures and gaits from the video by using a human body posture estimation algorithm and a gait recognition algorithm, fuses the advanced features and the primary features to obtain fused features, and clusters all detected student targets by using the fused features, thereby realizing cross-camera identity unification of the same student under different cameras, and improving the identification accuracy of the student. Face detection and recognition are carried out in each clustering group, a student information base is matched to determine student IDs, and the student IDs, video timestamps and monitoring camera position information are stored; and the client retrieves target information according to the input student ID or photo, and automatically clips and generates a time-space continuous complete cross-camera trajectory tracking video. According to the invention, efficient and intelligent analysis of campus monitoring data is realized, and targets can be accurately associated and tracked in a cross-camera scene.

The invention relates to the technical field of museum safety management, and discloses a museum intelligent monitoring system based on multi-source electronic information fusion, and the system comprises a multi-source electronic information collection module which obtains video monitoring data, environment sensor data and passenger flow monitoring data distributed in a museum; the data fusion module is used for extracting dynamic behavior characteristics from the video monitoring data, extracting abnormal environment parameters from the environment sensor data, extracting density distribution characteristics from the passenger flow monitoring data, and generating a comprehensive risk map through multi-modal characteristic fusion; the intelligent early warning module is used for generating graded early warning signals based on risk types contained in the comprehensive risk map; and the linkage control module is used for triggering corresponding control operations in security and protection equipment starting, environment regulation equipment regulation or passenger flow dispersion terminal prompting in the corresponding areas according to the graded early warning signals. The safety supervision and management efficiency of the museum can be improved, and various risks in the museum can be comprehensively, accurately and timely managed, controlled and handled.

A method of authenticating a video surveillance data package from a video management system (VMS) of a video surveillance system, the method comprising the steps of a) receiving, by a computing device, a video surveillance data package generated by the video management system; b) calculating, by the computing device, a first hash value for the received video surveillance data package; c) comparing the first hash value of the received video surveillance data package to a second hash value provided by a secure repository, the secure repository comprising a video surveillance data package received from the video management system prior to step a); d) validating the first digital signature of the received video surveillance data package; and e) determining that the video surveillance data package received by the computing device is authentic based on both step c) and step d).

A surveillance system of multiple video / audio source is provided, which includes a first surveillance device, a second surveillance device, a network video interface conversion device, and a user device. The first surveillance device acquires first surveillance data, and the second surveillance device acquires second surveillance data. The network video interface conversion device is connected to the first surveillance device and the second surveillance device, and sets the universally unique identifiers (UUIDs) and ports of the surveillance devices via a conversion protocol compliant with the open network video interface forum (ONVIF). The user device is connected to the network video interface conversion device, and the first and second surveillance devices are defined as independent network surveillance devices.

A method may comprise receiving airspace surveillance data from a plurality of sensors, the airspace surveillance data comprising tracks associated with one or more targets, aggregating the airspace surveillance data to obtain aggregated data, performing density-based clustering of the aggregated data to obtain a plurality of clusters, determining one or more candidate associations between the tracks and the clusters, associating each of the tracks with one of the one or more targets based on the candidate associations, and estimating a track for each of the one or more targets based on the associations between the tracks and the one or more targets.

The present invention discloses an intelligent highway inspection and control system based on multimodal data fusion, comprising a multimodal data acquisition module; a data fusion and preprocessing module; an intelligent inspection and analysis module; an inspection decision and execution module; and a system management and user interaction module. By integrating multiple data sources such as video surveillance data, vehicle traffic data, and meteorological data, real-time monitoring and intelligent analysis of highway traffic are achieved, and abnormal behaviors and violations of vehicles can be identified quickly and accurately, significantly improving inspection efficiency and accuracy. The system overcomes the defects of traditional inspection methods such as reliance on manual labor, low efficiency, insufficient accuracy, and poor real-time performance. It can discover and handle violations in real time, enhance the timeliness and deterrent effect of inspections, improve management efficiency and user experience, and thus effectively ensure the safety and smooth flow of highways, providing traffic management departments with an efficient and reliable intelligent inspection solution.

The invention provides an old people guarding method and system based on the Internet of Things, and the method comprises the steps: generating a control parameter distribution state of an Internet of Things node device according to the use data of the Internet of Things node device, the Internet of Things node device being a household intelligent device in communication connection with an Internet of Things central device; modeling based on the control parameter distribution state of the Internet of Things node equipment to obtain a behavioral habit model of a guarding object, monitoring daily use behaviors of the Internet of Things node equipment, and judging whether an abnormal use event which is not matched with the behavioral habit model exists or not; and when an abnormal use event which is not matched with the behavior habit model exists, calling video monitoring data to analyze the real-time state of the guarding object, and judging whether emergency treatment measures need to be executed or not according to the analysis result of the real-time state, so that the behavior data of the old people can be comprehensively obtained, and the safety of the old people is improved. Therefore, the physical state of the elderly can be analyzed and predicted more reasonably.

The present application discloses a crane anti-collision warning method, system, electronic device and storage medium. The method comprises the following steps: importing historical video surveillance data of the crane's surrounding environment into a lightweight network structure model for training; importing real-time monitoring data of the crane's surrounding environment into a trained target detection model for identification, thereby obtaining target information of the crane's surrounding environment; importing the target information into a binocular vision ranging algorithm model to obtain the real-time distance between the target object and the crane; and determining whether the real-time distance is less than a preset threshold. If so, performing corresponding warning processing on the crane. Compared with the prior art, the method has the following effects: by designing a lightweight target detection model and a binocular vision ranging algorithm, the method can improve the accuracy and efficiency of target recognition around the crane, thereby more accurately obtaining target information and real-time distance information, thereby improving the performance of the crane's anti-collision safety warning.

The present invention relates to the technical field of video analysis means, and specifically, to a method for real-time multi-target tracking of passengers in a terminal. This method is based on video surveillance data in the terminal, with passengers as the main tracking objects, and aims to complete real-time multi-target tracking in the terminal scenario. Secondly, the proposed multi-target tracking method can analyze the surveillance video in the terminal in real time and accurately track multiple passengers. This technology not only improves monitoring efficiency, such as being able to count passengers in real time and set early warning broadcasts, but also optimizes passenger flow lines, reduces congestion, and thus enhances the overall travel experience of passengers. Combined with the current aviation transportation industry's growing demand for safety and efficiency, this method will provide airport management departments with strong data and technical support, enabling them to respond to emergencies in a timely manner on the basis of real-time monitoring, and enhance the level of intelligence in airport operations.

Systems and methods for generating a large volume of synthetic stream-of-commerce security imaging data is disclosed. Methods for creating synthetic baggage x-ray scans, synthetic passenger millimeter wave scans, synthetic passenger video surveillance data, and introducing prohibited items to real security images are also disclosed.

The invention relates to the technical field of video stream encryption methods, in particular to a video stream encryption method for a video monitoring system. The problems that when a video stream encryption method is used, video monitoring data are generally stored and backed up through a storage server, but when the video monitoring storage and backup time is long, video files are prone to being lost and leaked, and potential safety hazards exist are solved. Compared with a traditional video stream encryption method for a video monitoring system, the video stream encryption method for the video monitoring system has the advantages that when the video monitoring storage backup time is long, a video file is prone to being lost and leaked, and large potential safety hazards exist; according to the video stream encryption method for the video monitoring system, multiple encryption and decryption methods are set, so that encryption of the monitoring video stream can be effectively realized, the security of video data is protected, the monitoring video can be effectively prevented from being lost, and security protection is increased.

The present application discloses a method, device, electronic device and medium for identifying employee status. The method includes: obtaining a surveillance video of a workstation to be identified; inputting the surveillance video into a pedestrian detection model to obtain pedestrian feature information; obtaining a target position corresponding to the pedestrian feature information and a reference position corresponding to the workstation to be identified, and determining the positional relationship between the target position and the reference position; if the target position is within the reference position, determining that the workstation to be identified is in an employee on-duty state, and being able to automatically identify the on-duty status based on the video surveillance data.

The present invention discloses a method for secure storage of video surveillance data based on blockchain technology, which relates to the technical field of video surveillance data storage. From the careful design of the hybrid storage architecture to the application of storage virtualization technology and then to the selection of highly reliable storage media, this application comprehensively ensures the integrity of data in the storage link, effectively preventing data corruption caused by hardware failures, data loss and other problems, and providing a solid data foundation for security decision-making. Through the reasonable allocation of computing power and the optimization of the encryption scheme, it is ensured that the data can be quickly decrypted and efficiently processed when needed, and various queries, analyses and decision-making requirements of security services can be responded to in a timely manner. High-priority data blocks, with the computing power preferentially allocated and a simple and efficient encryption and decryption process, quickly provide key clues to ensure that security personnel can obtain effective information in the first time, strongly supporting emergency response operations and achieving a perfect balance between data availability and security.

This application discloses a method, apparatus, electronic device, and medium for acquiring surveillance data. The method includes: if it is detected that a user has opened a target application on a terminal, then identifying the camera added to the target application; for the camera added to the target application, acquiring a first target camera that triggers an alarm event within a preset time period via a cloud server, and / or, selecting a second target camera from the cameras based on the user's historical viewing information of the camera's surveillance footage; and acquiring and pre-storing a data stream of the latest preset data volume from the target camera. This solution can predict the target camera that the user is likely to view based on alarm events and / or the user's historical viewing information of the camera, thereby pre-storing the target camera's data stream. When the user selects to view the camera's surveillance footage, it can respond quickly, improving the real-time performance of the surveillance footage and enhancing the user experience.

The application provides a communication method for slave station active notification of master station data reading under Modbus protocol, and the steps are as follows: the master station sends the Modbus address of the data to be monitored to the specific address of the slave station; after receiving the data to be monitored, the slave station carries out data monitoring, directly records the change result in the specified address segment, and changes the data state bit used for identifying the data change monitored by the slave station; the master station periodically reads the data state bit of the slave station, reads the data in the specified address segment of the slave station when the change is found, and obtains the data with value change of the slave station; the slave station changes the data state bit after feeding back the data in the specified address segment to the master station; and the master station completes the analysis and record of the changed data. In the application, the master station of Modbus communication only needs to periodically scan the identification bit used for indicating the state of the slave station, so that the purpose of the slave station notifying the master station of the event occurrence and reading the parameters can be achieved, and the application has the advantages of scientific design, strong practicability, simple operation and high communication efficiency.

The present application relates to the field of data acquisition and processing, and in particular to a data acquisition method, system, and storage medium for smart buildings, including: periodically acquiring surveillance videos of corresponding areas through multiple camera units, and identifying identification information of each target object in each surveillance video; obtaining a current behavior probability value for each target object, and extracting key image frames and warning videos from the surveillance video based on the current behavior probability value for each target object; annotating the key image frames and warning videos, and mapping them to a three-dimensional map of the smart building according to the position of the camera unit; acquiring a complete movement path map for each target object; and sending the complete surveillance path map corresponding to the target object to a data receiving end. The present invention solves the problem of large amounts of surveillance data and the inability to obtain warning information in a timely manner, thereby reducing the amount of surveillance data and enabling timely acquisition of warning information.

A method of training a machine learning algorithm to identify objects or activities in video surveillance data comprises generating a 3D simulation of a real environment from video surveillance data captured by at least one video surveillance camera installed in the real environment. Objects or activities are synthesized within the simulated 3D environment and the synthesized objects or activities within the simulated 3D environment are used as training data to train the machine learning algorithm to identify objects or activities, wherein the synthesized objects or activities within the simulated 3D environment used as training data are all viewed from the same viewpoint in the simulated 3D environment.

The present invention relates to the field of video detection technology, and specifically to a method and system for detecting and recognizing surveillance videos based on deep learning, including: obtaining a target surveillance video of a surveillance area based on a surveillance camera device, obtaining a video frame sequence of the target surveillance video, and performing target screening on the video frame sequence to obtain a video frame set; performing target detection on each video frame in the video frame set based on a trained target detection model to obtain a target detection result set for each video frame in the video frame set. Through the combination of keyword extraction and a large language model, the present invention can convert complex surveillance data into structured event descriptions. Such textual information not only facilitates managers to quickly understand the on-site situation, but also provides a reference for subsequent event processing. Combining with the real-time generated event descriptions, managers can formulate response strategies more efficiently.

A method for data retention comprises obtaining, from a first retention system having a first retention policy associated therewith, surveillance data generated by surveillance of a location where a surveillance system is deployed, identifying a level of interest in the surveillance data, and, in response to identifying, based on the level of interest, that at least part of the surveillance data is of interest, causing the at least part of the surveillance data to be stored in a second retention system, the second retention system having associated therewith a second retention policy different from the first retention policy.

The present application discloses verification of an updated analysis program in a monitoring system. Specifically, a method (100) for evaluating an updated analysis program in a monitoring system is provided, the monitoring system including a plurality of monitoring devices arranged to monitor similar environments. The method comprises: identifying (S110) available processing resources, selecting (S120) a first monitoring device for which available processing resources have been identified, selecting (S130) a second monitoring device, obtaining (S140) monitoring data by the second monitoring device, and executing (S150) a current analysis program on the monitoring data. The method also comprises sending (S155) the monitoring data to the first monitoring device, executing (S160) an updated analysis program on the monitoring data in the first monitoring device, and evaluating (S190) the updated analysis program based on the results of the current analysis program and the updated analysis program.

The invention relates to the field of power transformation infrastructure security and protection, in particular to a power transformation infrastructure risk identification method and system and a medium, and the method comprises the steps: collecting multi-modal data of a construction site, the multi-modal data comprising video monitoring data, operation monitoring data, equipment state data and environment monitoring data; a BIM model is adopted as a three-dimensional digital base map of a construction site, fusion modeling is carried out on the multi-modal data and the BIM model, and the BIM model after fusion modeling is obtained; inputting the multi-modal data into a pre-trained AI large model, extracting safety risk characteristics and violation behavior modes of the construction site through the AI large model, and identifying safety risks and violation behaviors in real time by the AI large model based on the extracted safety risk characteristics and violation behavior modes; and in response to the AI large model, identifying security risks and violation behaviors in real time, generating alarm information, and positioning and displaying the alarm information through the BIM model after fusion modeling.

The invention relates to the technical field of early warning of main power transformation equipment, in particular to a method for early warning and dynamic tracking monitoring of main power transformation equipment, which comprises the following steps of: according to the data fluctuation condition of the same data type as a chain node in an early warning association chain of an abnormal interference risk source in the equipment state information of the main power transformation equipment, determining an abnormal interference risk source according to the data fluctuation condition of the same data type as the chain node in the early warning association chain; and analyzing the risk confidence value of the early warning association chain of each abnormal interference risk source based on the corresponding abnormal interference risk source. According to the invention, the abnormal interference risk source set corresponding to the abnormal state summary information can be locked according to the abnormal fluctuation item in the real-time operation parameter monitoring result of the power transformation main equipment; and an early warning association chain of each abnormal interference risk source in historical maintenance data is combined to dynamically construct an early warning tracking and monitoring object set, an administrator is assisted to generate a power transformation main equipment tracking and monitoring object screening decision, and effective management of early warning and dynamic tracking and monitoring data of the power transformation main equipment is realized.

The invention relates to the technical field of fire safety, and discloses an intelligent recommendation method and system for a fire escape route, and the method comprises the steps: integrating a building information model, real-time sensor network data and video monitoring data, and constructing a virtual digital twin environment which is synchronously updated with a physical fire scene; preprocessing the multi-source signal; based on the preprocessed multi-source signal, identifying an open fire position, smoke concentration distribution and personnel aggregation through a convolutional neural network, and generating a real-time and multi-dimensional environment risk map and a personnel distribution map; parallel deduction is carried out in a digital twin environment, an escape route is planned by adopting an A * algorithm, and an optimal personalized escape route is obtained; the change of the environment and the personnel state is continuously monitored, and when a new congestion occurs, an escape route is dynamically planned and pushed in real time; according to the invention, the safety and efficiency of personnel evacuation in a fire scene are improved.