155 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.

Systems and methods are for mitigating hindsight bias related to designing and using artificial intelligence models for outlier events. More specifically, systems and methods for the use of synthetic data in the training and / or validation of model predictions in order to prevent overfitting and generate predictions that attempt to predict the unpredictable.

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 present invention relates to a targeted perception-oriented twin substructure interaction method and system, and application. The method includes: acquiring multivariate inspection and monitoring data and finite element influence line data of a main structure; solving, on the basis of the inspection and monitoring data and the finite element influence line data of a non-focus region, boundary conditions of a focus region; establishing, for the focus region, a refined twin substructure finite element model, and correcting material properties of the refined twin substructure finite element model on the basis of the inspection and monitoring data and the finite element influence line data of the focus region; and calculating a correction force on the basis of the boundary conditions of the focus region and the material properties, and using the correction force as an equivalent external load to act on nodes of a global finite element model.

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.

This document describes a method for identifying anomaly constructs in real-time based on multimodal surveillance data, the identification being done using a multimodal sensory and cognitive abstraction module. Meta-descriptors are generated for the surveillance data and subsequently, meta-descriptor embeddings are generated for the meta-descriptors whereby the meta-descriptor embeddings are used by an anomaly assertion model to detect anomaly constructs within the surveillance data.

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 relates to a two-way early warning method and system for work depot operation, processing equipment and a storage medium, and the method comprises the steps: arranging a vibration sensor on each track, and enabling the vibration sensors of all tracks in a certain region to form a corresponding vibration sensor array; the signals collected by the vibration sensors are processed, the vibration signals, collected by the vibration sensors, of the train passing through the track are extracted, and the track of the coming train is determined; a computer vision algorithm is adopted, based on the video monitoring data of the track of the coming train, the behavior state of a working worker in operation in the track is detected, and the behavior state is sent to a train center controller of the coming train; the train center controller of the coming train judges whether the working personnel in operation have potential threats or not according to the detected behavior state of the working personnel in operation in the track, if yes, early warning is carried out, and the method can be widely applied to the field of operation scenes of the railway service depot.

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.

Disclosed is a method and a system for safety management of a gas pipe well based on a smart gas IoT. The method may be executed by a processor and include: obtaining an environmental information sequence of the gas pipe well and an in-well monitoring data sequence by controlling a monitoring component according to a monitoring parameter based on the smart gas IoT, and storing the environmental information sequence and the in-well monitoring data sequence in a storage unit; determining an in-well risk value; adjusting the monitoring parameter in response to determining that the in-well risk value exceeds a risk threshold and updating the in-well risk value; determining an impact degree of a gas component; determining a pipe well inspection program and generating a corresponding pipe well inspection instruction to be sent to an inspection terminal; and adjusting the monitoring parameter and clearing expired data in the storage unit.

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).

Provided is a smart surveillance system that includes one unmanned aerial vehicle (UAV), a plurality of Internet of Things (IoT) terminals distributed in a surveillance area, and a plurality of base stations distributed in the surveillance area, wherein the UAV selects any IoT terminal from among the plurality of IoT terminals using deep learning auction training, receives surveillance data from the selected IoT terminal, and transmits the surveillance data to a data center through the Internet and any one base station among the plurality of base stations.

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.

The invention relates to the technical field of intelligent traffic, and provides a traffic jam sensing method and system based on ETC portal data, and the method comprises the steps: dividing a road segment based on an ETC portal, and determining a bidirectional portal sequence; identifying daily high-frequency portal pairs according to the vehicle passing amount, screening the daily high-frequency portal pairs, constructing a road network congestion fusion sensing portal sequence library, and updating the road network congestion fusion sensing portal sequence library; activating a daily high-frequency portal pair of the road network congestion fusion sensing portal sequence library, and based on road section passing time, performing combined sensing on the activated daily high-frequency portal pair by adopting a slope analysis method and a standard deviation analysis method to determine a jammed portal pair road section; and aiming at the road section which is determined to be congested, carrying out image identification through video monitoring data, and determining a specific congested road section. According to the invention, the approximate congestion road section is identified through the ETC door frame data, the pile number of the congestion road section is accurately identified through the video monitoring data, and the congestion event can be quickly found and handled.

The present disclosure is related to endpoint monitoring and forensic artifact collection. In some embodiments, forensic artifacts and endpoint monitoring data are collected on an endpoint using the same agent. In some embodiments, forensic artifacts are chunked prior to being transferred to a cloud server for analysis. In some embodiments, forensic artifacts are categorized and processed according to the category. In some embodiments, the agent operates in memory and does not write to disk. In some embodiments, the agent does not write to disk during the transfer of forensic artifacts to a cloud server. In some embodiments, a cloud server can enable natural language queries of monitoring data and / or forensic artifacts. In some embodiments, the cloud server provides summaries. In some embodiments, the cloud server identifies the most relevant data in monitoring data and / or forensic artifacts.

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.

To determine defects during molding by combining a monitoring data set and waveform data in the same dimension, the setting time data acquisition module 112 is configured to acquire a variety of setting time data 91, which are monitoring data at preset times among the monitoring data obtained when a molded article is formed by an injection molding machine 1; a waveform data acquisition module 113 is configured to acquire waveform data 95, which are the monitoring data that are continuous within a predetermined period among the monitoring data; a sample point data acquisition module 114 is configured to acquire a variety of sample point data 97, which are data at a variety of sample points 96 in the waveform data 95;A combination data generation module 115 is configured to combine a monitoring data group 92, which contains the multitude of parts of setting time data 91 and the multitude of parts of sampling point data 97, to generate combined data 90; and a pass / fail determination module 127 is configured to determine whether a defective item has occurred in the formed item, based on the combined data 90 contained therein.

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 is provided with: a master station for transmitting and receiving data to and from a control unit; and a plurality of substations for transmitting and receiving data to and from the master station by means of a synchronous transmission system via a common transmission line. A control monitoring data area, which is assigned to each of the substations in a data transmission direction determined, and a management data area, which is different from the control monitoring data area and is used for transmitting and receiving data to and from all of the substations, are provided. In the control monitoring data area, an address is set as a timing for acquiring a data area allocated to the slave station, the master station transmits a check identifier using the management data area at a timing after execution of a process for detecting the address, and the slave station transmits the check identifier to the slave station when receiving the check identifier. When an operation identifier is transmitted from the master station, data transmission and reception using the control monitoring data area are enabled, the verification identifier is recorded and stored, and when the operation identifier is transmitted from the master station, data transmission and reception using the control monitoring data area are enabled or disabled on the basis of the verification result of the operation identifier and the verification identifier.

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 discloses a hydroelectric generating set health state evaluation system based on multi-source information fusion, relates to the technical field of hydroelectric generating set health state evaluation, and determines that equipment of the same type is in any one of a basic time period, a binary time period and a final time period under the condition of different service durations through a stage analysis unit. Then, analyzing and starting the content of the monitoring object under the condition of different time periods; a monitoring object analyzes a plurality of times of maintenance data, the occurrence frequency of each piece of monitoring data under the abnormal condition is observed, and according to the occurrence frequency, which monitoring objects appear at high frequency is determined, so that monitoring of different data in different stages is determined; therefore, the method can observe a small amount of data under different conditions to judge whether abnormity occurs or not, is simple and effective, does not need to monitor all data, and can save the computing power under part of conditions.

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.