2290 results about "Surveillance camera" patented technology

The invention relates to the technical field of safety monitoring, in particular to a smart park safety management method and system based on AI visual identification, and the method comprises the following steps: obtaining a video stream captured by a monitoring camera of a smart park, extracting the movement track data of a person through AI, and carrying out the statistics of the stay duration and access times of a target object in each grid; and constructing retention heat distribution of the grid region to obtain a personnel trajectory retention heat value. According to the invention, through the data processing of the monitoring video stream and the combination of the gridding analysis of the personnel motion trail, the accurate capture of the abnormal cruise behavior is enhanced, and through the calculation of the change rate of the access times between the adjacent grids, the statistics of the round-trip condition of the target among the plurality of areas and the combination of the passing authority data, the abnormal screening is carried out. The method improves the recognition capability of abnormal movement modes, achieves the dynamic adjustment of regional safety risk levels, improves the response capability of park security, enables the recognition capability of abnormal behaviors to be enhanced, and reduces the false alarm rate.

The invention discloses a human abnormal behavior monitoring method based on a large-model multi-agent, which is executed by a modular multi-agent system deployed on a back-end server, obtains information through a monitoring camera, and comprises the following steps: obtaining a video stream from the monitoring camera by a sensing agent and extracting human body posture features; analyzing the key frame by a scene understanding agent by using a visual large model, and constructing a time sequence dynamic scene graph; the core reasoning agent evaluates the scene semantic conformity based on the pre-trained large model and performs abnormal preliminary judgment; performing fine-grained classification, interpretation generation and risk assessment on the abnormal behaviors; and the report and action agent generates an alarm and records event data. According to the invention, through multi-agent cooperative work and a large model technology, efficient and accurate monitoring of human abnormal behaviors is realized, and the intelligent level of the monitoring system and the abnormal behavior identification accuracy are improved.

The invention discloses an intelligent emergency and management and control method for a stadium, and belongs to the technical field of stadium safety management and control. According to the method, video stream data are collected through a monitoring camera array, and coverage areas of all cameras in the array have 15%-20% of overlapping areas; inputting the data into a pre-trained people density identification model to generate a real-time people flow distribution thermodynamic diagram; the retention index of each partition is calculated, and an emergency evacuation mode is activated in combination with a preset safety threshold value; in the emergency mode, the infrared sensor group monitors the instantaneous pedestrian flow speed, and an optimal evacuation path is generated by a dynamic path planning algorithm; the path is converted into a control instruction to drive an electromagnetic access control gate, emergency lighting and an electronic guide identifier, feedback data triggers a path re-calculation mechanism, and a two-dimensional code scanning device counts the number of evacuated people so as to start manual re-checking. Real-time situation awareness, dynamic risk assessment and intelligent evacuation guidance of the stadium are achieved, emergency response and evacuation efficiency is effectively improved, and crowd safety is guaranteed.

The invention discloses a security area intrusion early warning method and system based on video images, and belongs to the technical field of intrusion early warning. Video image data are collected in real time through monitoring cameras distributed in a security area, track recording is carried out on an intrusion target by using a preset target detection algorithm, the motion direction, speed change and stay time of the intrusion target are analyzed, a dynamic motion track is generated, and the scene layout in the area is combined, so that the security of the intrusion target is improved. Speculating an intrusion path by using the trajectory prediction model; evaluating danger levels of different potential paths, and sorting the paths according to priorities; according to the path priority, the early warning sensitivity of the monitoring area is adjusted in real time, and if the path analysis result is a high-risk area, an early warning system is triggered, and alarm information is sent out. According to the method, the high-risk path is effectively identified through comprehensive risk assessment and dynamic path sorting; and on the basis of a sensitivity dynamic adjustment strategy and a multi-level early warning mechanism, the real-time performance and the intelligence of the monitoring system are remarkably improved.

The invention discloses a method for determining a preset position of a video monitoring camera. Camera presetting positions usually need to be manually set one by one, and time and labor are wasted. The method is used for a video monitoring system, and automatic setting of the preset position of the camera is realized by utilizing a group of scene images and the to-be-monitored target image, so that the efficiency is greatly improved. The method comprises the following steps: acquiring feature points based on a scene image set by adopting a computer vision technology, generating a three-dimensional model of a scene by adopting a motion recovery structure technology and a surface reconstruction algorithm, and obtaining the position of a to-be-monitored target in a three-dimensional space by matching the feature points of a to-be-monitored target image with the feature points of the scene image set. Therefore, a proper camera is selected, a corresponding preset position is calculated, and finally the preset position is corrected according to an actual monitoring effect.

The invention relates to the technical field of video detection, in particular to a monitoring video detection and identification method and system based on deep learning, and the method comprises the steps: obtaining a target monitoring video of a monitoring region based on monitoring camera equipment, obtaining a video frame sequence of the target monitoring video, and carrying out the target screening of the video frame sequence, so as to obtain a video frame set; and 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 of each video frame in the video frame set. According to the method, through combination of keyword extraction and a large language model, complex monitoring data can be converted into structured event description, the textualized information not only facilitates management personnel to rapidly know the field condition, but also can provide reference for subsequent event processing, and the event description generated in real time is combined, so that the management personnel can conveniently and rapidly know the field condition. And a manager can make a coping strategy more efficiently.

The invention is suitable for the technical field of fall detection and help calling, and provides a fall detection and help calling method for the elderly at home, and the method comprises the steps: collecting the multi-source health monitoring data of the elderly through a monitoring camera and a sensor, and carrying out the preprocessing based on the collected data; respectively extracting walking posture features, getting-up and sitting-down posture features, daily activity posture features, motion features, acceleration features, gyroscope features and heart rate features of the elderly; inputting the extracted features into a trained abnormal action detection model for action detection; when the old people have abnormal behavior actions, performing multi-dimensional analysis on the abnormal behavior actions of the old people by utilizing a clustering algorithm; and feeding back the analysis result of the abnormal behavior action of the old person in real time according to a preset help calling rule. According to the invention, the aims of comprehensively monitoring the health state of the elderly, timely discovering abnormity, deeply analyzing reasons and quickly responding to rescue can be fulfilled.

The invention discloses a mangrove forest disease and insect pest remote monitoring and intelligent prevention and control system based on the Internet of Things. A plurality of modules such as an Internet of Things sensor network, an unmanned aerial vehicle inspection system, a high-definition monitoring camera network, intelligent prevention and control equipment and a software platform are integrated; the Internet of Things sensor network monitors environmental parameters and pest and disease damage signs in real time, the unmanned aerial vehicle inspection system provides aerial inspection support, and the high-definition monitoring camera network realizes 24-hour uninterrupted monitoring on the ground; the software platform is responsible for data acquisition, transmission, analysis and early warning release, and provides a remote control interface for management personnel to perform accurate prevention and control; the intelligent prevention and control equipment automatically or remotely executes prevention and control tasks according to the monitoring data and the early warning information; the system also has additional functions of energy management, network communication, user authority management and the like, and continuous and stable operation and data security of the system are ensured. The implementation of the system can effectively improve the intelligent level of mangrove forest pest control, and provides powerful support for protecting a mangrove forest ecological system.

The invention relates to the technical field of fire alarm, in particular to a fire ground-air integrated monitoring and early warning system and method, and the system comprises a flame and smoke recognition module which collects image data from a ground monitoring camera and an air unmanned aerial vehicle, carries out the feature extraction of the image data, generates a feature data set, and carries out the early warning based on the feature data set. And judging existence of flames and smog by using image analysis, and generating a fire identification result. According to the invention, multi-source heterogeneous data obtained by the ground monitoring camera, the aerial unmanned aerial vehicle and the infrared thermal imaging equipment are integrated for collaborative analysis, so that high-precision feature extraction of fire hazards and judgment of fire behaviors are realized, and the accuracy and response efficiency of fire recognition are improved; the unmanned aerial vehicle is used for dynamically collecting air thermal imaging data in real time, and an alarm is quickly formed based on temperature abnormal points, so that an abnormal temperature area can be timely positioned and pre-warned in the early stage of a disaster.

Disclosed is a image processing apparatus and a method for controlling the image processing apparatus. The image processing apparatus according to an embodiment of the present disclosure may identify an object from an acquired image, determine whether the object is hidden by another object by using an aspect ratio of a bounding box of the detected object, and based on the object being hidden, estimate an entire length of the object based on coordinate information of the bounding box. Accordingly, the size information of the hidden object may be efficiently identified while a large amount of database is applied or resources of the apparatus is minimized. The present disclosure may be in connection with a surveillance camera, an automotive driving vehicle, an artificial intelligence module of at least one of a user terminal or a server, a robot, an augmented reality (AR) device, a virtual reality (VR) device, a device related to a 5G service, and the like.

The invention relates to the technical field of anti-terrorism early warning, in particular to an intelligent anti-terrorism early warning system based on image recognition and big data analysis, and the system comprises a multi-source data collection module which is used for collecting video streams, image data and environment parameters through a monitoring camera, a sensor and an Internet of Things device in a deployment region, executing target detection and desensitization operation at the edge computing node, and caching original data to a distributed message queue; and the data standardization processing module is connected with the message queue and is used for carrying out space-time alignment and semantic mapping on the multi-modal heterogeneous data. According to the invention, monitoring videos, sensor data, Internet of Things information and the like are preprocessed and desensitized at edge nodes, space-time alignment and semantic unification are completed through a data standardization processing module, and cross-modal feature fusion is realized through a multi-modal fusion analysis module, so that barriers among different types of data are broken through, and the accuracy of data fusion is improved. Scattered information forms an organic whole, and a comprehensive and consistent data basis is provided for abnormal behavior recognition.

The invention relates to the technical field of video processing, and discloses a panoramic monitoring camera video processing method and device based on cloud storage, and the method comprises the steps: carrying out the coding compression and layering processing of original video data collected by a panoramic monitoring camera, obtaining an initial data set, and executing the calibration frame parameter optimization calculation, camera calibration parameters are obtained; generating hierarchically stored processing data according to the initial data set and the camera calibration parameters; performing multiband fusion and triangulation calculation on the calibrated panoramic video data to obtain a three-dimensional scene digital model; analyzing the hierarchically stored processing data and the three-dimensional scene digital model to obtain a monitoring object behavior prediction result; according to the method, differential modeling and behavior prediction of panoramic monitoring on different areas are realized, smooth user experience is provided, and video access delay and operation complexity are reduced.

The present invention relates to the field of building security, and discloses a safety protection method and system for building construction, comprising: a construction information acquisition module, which collects environmental information and construction personnel information of the current building construction area based on a monitoring camera and a sensor group; a construction information processing module, which processes the environmental information and construction personnel information obtained by the construction information acquisition module to obtain a subjective risk factor and an objective risk factor respectively; a construction risk analysis module, which obtains a construction risk index by comprehensive calculation based on the subjective risk factor and the objective risk factor, and compares the construction risk index with a preset construction risk interval. If the construction risk index is within the construction risk interval, the subjective risk factor or the objective risk factor is further analyzed. If the construction risk index exceeds the construction risk interval, the current building construction area is immediately warned; and a construction warning module sends a warning signal according to the judgment result of the construction risk analysis module.

The invention discloses a locomotive sanding detection method. The method comprises the following steps: step 1, judging whether a license plate number is identified or not; 2, broadcasting sanding notifications of all the positions according to a preset time difference; step 3, acquiring a video shot by the monitoring camera; 4, dark light enhancement is carried out through a U-Net model, a YOLOV5 model is input, and whether the monitoring camera is shielded or not is judged; step 5, carrying out illumination adaptation processing on a plurality of continuous frames of images of the video obtained in the step 3 through a CLAHE algorithm; step 6, carrying out image enhancement through a Retinex algorithm and a U-Net model; 7, inputting a 3D-CNN model to carry out feature extraction and time sequence modeling; and step 8, inputting a Transform Encoder model, and outputting the probability of each sanding flow rate. According to the method, the detection of the sand spraying amount can be completed, and the detection of whether the monitoring camera is shielded or not can be completed before the detection of the sand spraying amount.

The invention discloses a camera monitoring system based on wireless communication and a communication protocol optimization method, and relates to the technical field of communication optimization, the camera monitoring system is composed of a monitoring camera group, a wireless communication network, an edge computing unit, a cloud data center and a user interaction terminal; the monitoring camera group simulates human eye vision, adjusts a visual angle and a resolution ratio according to needs, and collects data; a wireless communication network is combined with quantum encryption and a traditional technology, and a communication mode is intelligently selected; the edge calculation unit processes data with low power consumption by using a neuromorphic chip; the cloud data center fuses the block chain and federal learning analysis data; the user interaction terminal has holographic projection and emotion perception functions and provides personalized services. According to the invention, the modules are collaboratively innovated, the pertinence of collected data is strong, the communication is efficient and safe, the processing is fast and accurate, and the interaction experience is good; the system has the functions of intelligent analysis, energy-saving management and the like, the intelligence and reliability of the system are improved, and the system has wide application prospects and market value.

The invention discloses an intelligent monitoring method and device for a video monitoring system based on cloud edge collaboration, and relates to a video monitoring technology. An edge computing platform is deployed on one side of the video monitoring system, so that the computing load of a central cloud platform is reduced; through combination of video fault data, historical operation data and real-time operation data, a correlation index between a picture parameter and a video fault type is calculated so as to accurately determine a video picture fault reason. Constructing a panoramic canvas through equipment parameters, and constructing a panoramic background model based on historical operation data; the real-time operation data and the panoramic background model are combined to continuously monitor faults of the holder and the zoom lens, and monitoring blind areas or picture loss caused by faults of the video monitoring system are avoided; the fault evaluation platform receives the detected suspected fault data and suspected fault indexes and feeds back the suspected fault data and suspected fault indexes to the fault alarm system in time, corresponding processing measures are taken, and the reliability of the monitoring camera system can be ensured.

The invention provides a video feature analysis method based on a multi-modal large model, and relates to the technical field of video feature analysis methods. Comprising a video data acquisition module, a multi-modal data preprocessing module, a multi-modal large model construction and training module, a video feature extraction and fusion module, a feature analysis and classification module, a result visualization module, a model evaluation and optimization module, a data storage and management module, a user interaction module and a system security protection module. The video data acquisition module can improve the accuracy of feature analysis from various video sources such as a monitoring camera, a network video platform and a local video file library, features and semantics in videos can be captured more comprehensively by fusing multi-modal information and a powerful multi-modal large model, and compared with a single-modal analysis method, the video data acquisition module has the advantages that the accuracy of feature analysis is improved. The recognition accuracy of objects, scenes and behaviors in the video is remarkably improved, misjudgment and missed judgment conditions are reduced, and a more reliable basis is provided for subsequent decision making.

1. The name of the product of this design: Bird feeder camera (N012, N013, N014). 2. Purpose of this design product: used for a surveillance camera device. 3. The design key point of this appearance design product lies in the shape. 4. The picture or photo that best illustrates the key points of the design: Design 1 Stereoscopic drawing 1. 5. Designate Design 1 as the base design.

The invention discloses a vehicle cross-lens tracking identification method and device, equipment and a storage medium. Comprising the following steps: tracking and identifying a vehicle in a video frame shot by a current camera based on a pre-trained target detection model; when the vehicle passes through a preset overlapping line in the cameras, matching the vehicle identified by the current camera and the vehicle identified by the adjacent previous camera in pairs by adopting a Hungary algorithm to obtain a vehicle matching result; matching results of the same vehicle in all the cameras are connected in series, and a cross-lens vehicle movement track is constructed. Based on a deep learning model, a Hungary algorithm and other methods, effective identification and accurate tracking of the same vehicle among different monitoring cameras are realized, the automation and precision of the system are greatly improved without adding hardware facilities, and powerful support is provided for traffic management.

A mechanism for implementing and demonstrating compliance with contagion-related protocols, the mechanism comprising, a temperature screening and monitoring sub-mechanism, a proximity screening and monitoring sub-mechanism, and a contact tracing sub-mechanism. Personal proximity beacons monitor distances between individuals to ultimately provide alerts. A dashboard allows for real-time monitoring and compliance demonstrating. Mask wearing and body temperature is monitored via surveillance cameras (traditional and thermal).

The invention relates to the technical field of camera control, and discloses an outdoor monitoring camera control method and device based on solar panel power supply, and the method comprises the steps: inputting original image data collected by a monitoring camera into a Light-EMA module for feature enhancement and Haar wavelet downsampling processing, obtaining a target feature matrix, and obtaining a target feature matrix; the optimal parameters of the holder control are calculated; collecting solar voltage and current values and environment illumination intensity, and performing SOC calculation and LSTM-GRU neural network prediction to obtain an energy availability index; performing multi-objective optimization solution through a DSAC-CAL algorithm to obtain an optimal decision vector of a system action space; according to the optimal decision vector, the camera frame rate, the processor frequency, the wireless transmission power and the sleep period are regulated and controlled, a multi-module cooperative control signal is obtained, the position prediction precision is improved, the system energy consumption is effectively reduced, the working time of the solar camera is prolonged, and the all-weather operation reliability is improved.

The invention discloses an intelligent terminal monitoring system and method based on edge calculation, and relates to the technical field of terminal monitoring, and the method comprises the steps: calculating the important coefficient of a camera through obtaining the monitoring region data of a traffic monitoring camera corresponding to each fault edge calculation center; screening out a target edge calculation center capable of receiving data of the fault edge calculation center from the non-fault edge calculation centers; calculating an adaptation coefficient of the target edge calculation center and the fault edge calculation center; according to the adaptation coefficient and the importance coefficient, determining a final target edge calculation center of each fault edge calculation center, and transmitting traffic monitoring camera data to the center for analysis; and finally, the target edge calculation center analyzes the data by combining the important coefficient of the camera, and monitors whether the camera has a fault or not. The traffic monitoring camera data can be timely and efficiently processed, the edge computing center can reasonably distribute the load, and the stability and the accuracy of the monitoring system are guaranteed.

The invention relates to a high-definition monitoring camera tracking method and system for multi-angle face detection. The method comprises the following steps: preprocessing an original image collected by a high-definition monitoring camera to obtain a first preprocessed image and a second preprocessed image; performing feature extraction and feature fusion on the first preprocessed image and the second preprocessed image to obtain a multi-scale fusion feature map; face positioning and posture analysis are carried out through a position sensitive convolutional network, and a face detection result is obtained; performing face region subdivision and angle characteristic analysis on the face detection result to obtain enhanced face representation; and calculating a cross-camera matching cost matrix based on the enhanced face representation, and performing trajectory prediction and updating processing to obtain a face tracking result in the multi-camera environment. According to the method, the capability of capturing facial micro-expressions and fine features is improved, high-precision face association and smooth trajectory handover among different cameras are realized, and the problem of trajectory fragmentation in view crossing areas of the cameras is solved.

The invention discloses a chemical production safety monitoring device, and relates to the technical field of safety monitoring, the chemical production safety monitoring device comprises a detachable monitoring mechanism and a monitoring camera installed at the bottom of the detachable monitoring mechanism, one side of the detachable monitoring mechanism is provided with a monitoring adjusting mechanism, and the monitoring adjusting mechanism is internally provided with a cooling fan. The detachable monitoring mechanism comprises a monitoring mounting bracket, an external power line is fixedly connected to one side of the bottom of the monitoring mounting bracket, a mounting sleeve is fixedly mounted on the side, away from the external power line, of the monitoring mounting bracket, and a clamping threaded ring is rotated to move to the top of the mounting sleeve; a clamping hook is separated from a fixed insertion hole by pressing the top of a clamping rotating arm under the action of a first rotating support, a connecting insertion block and a monitoring camera can be taken out from the interior of a mounting sleeve, and real-time monitoring is performed by mounting the monitoring camera on a worker, so that monitoring dead angles are avoided.

The invention relates to the technical field of building data processing and virtual arrangement, in particular to a monitoring camera virtual arrangement method and system based on a BIM technology. The method comprises the following steps: acquiring a BIM data set; performing data preprocessing on the BIM data set to obtain a standard BIM data set; building three-dimensional structure information of the standard BIM data set is extracted, and building three-dimensional structure data is obtained; and performing spatial layout analysis on the three-dimensional structure data of the building to obtain spatial layout data, and performing spatial mapping on the three-dimensional structure data of the building according to the spatial layout data to generate a three-dimensional space map of the building. Therefore, through the Internet of Things technology, the data mapping technology, the virtual arrangement technology and the visualization technology, dynamic influence analysis is carried out on the environment characteristics and the scene state of the building so as to cope with the complex and changeable environment conditions of the building; and different scene requirements are met.

The present invention discloses an intelligent terminal monitoring system and method based on edge computing, which relates to the technical field of terminal monitoring. By obtaining the monitoring area data of traffic monitoring cameras corresponding to each faulty edge computing center, the importance coefficient of the cameras is calculated; and a target edge computing center capable of receiving the data of the faulty edge computing center is screened out from each non-faulty edge computing center; the adaptation coefficient between the target edge computing center and the faulty edge computing center is calculated; according to the adaptation coefficient and the importance coefficient, the final target edge computing center of each faulty edge computing center is determined, and the traffic monitoring camera data is transmitted to this center for analysis; the final target edge computing center analyzes the data in combination with the importance coefficient of the camera to monitor whether the camera is faulty; it ensures that the traffic monitoring camera data can be processed in a timely and efficient manner, and at the same time ensures that the edge computing center can reasonably allocate the load, guaranteeing the stability and accuracy of the monitoring system.

The invention relates to the technical field of rainfall data forecasting, in particular to a rainfall observation and evaluation method for global coverage of a high-density city. The method comprises the following steps: performing monitoring point position analysis on a target city to obtain a camera monitoring point set; performing monitoring camera selection on the target city according to the camera monitoring point set to obtain a raindrop monitoring camera identifier set; wherein the raindrop monitoring camera identifier set comprises a plurality of raindrop monitoring camera identifiers; carrying out real-time rainfall image stream acquisition on the corresponding camera according to the raindrop monitoring camera identifier to obtain a real-time rainfall image sequence; and performing automatic raindrop edge identification on the raindrops based on the real-time rainfall image sequence to obtain a raindrop edge identification result graph. According to the invention, the limitation of the traditional rainfall station-shaped distribution is overcome, the whole-city non-blind-area rainfall monitoring is realized, and the method has the advantage of high spatial resolution.

The invention discloses a method for automatically detecting red tide of Paleocystis globosa based on video monitoring, which comprises the following steps of: S1, acquiring image data of a sea surface by using a high-definition monitoring camera on a buoy platform, transmitting the image data to a database in real time, and preprocessing the image data; s2, making a labeling scheme, labeling the preprocessed image data, and constructing a training sample library; s3, training and optimizing the YOLOv8 target detection model to obtain a globocystis globosa recognition model; s4, carrying out frame extraction processing on a video monitoring picture to be identified according to a certain time interval, inputting each frame of picture into the globocystis globocystis identification model for identification, and automatically generating globocystis globocystis red tide monitoring information; according to the method, a video monitoring technology and an artificial intelligence target detection algorithm are combined, so that efficient and accurate monitoring of capsules generated during red tide of the globocystis globocystis can be realized, and the real-time performance and the automation level of red tide monitoring of the globocystis globocystis can be remarkably improved.

A cabinet may include a body, a bracket connected to a rear surface of the body, and a cover connected to a front surface of the body to be opened and closed, wherein the body may include a first accommodation groove at a rear edge of the body, the bracket may include an insertion protrusion corresponding to the first accommodation groove and a support protrusion at the edge of the bracket to face a rear surface of the body at a position spaced apart from the first accommodation groove. The body may slide to a second position from a state where the body is temporarily fixed to the bracket at a first position.

The invention provides an elderly nursing method and system, which is applied to elderly nursing services, and comprises the following steps: receiving a video link generated when a monitoring camera monitors an identification object through a message push service; acquiring user configuration information from a database; sending the video link and the user configuration information to an artificial intelligence cloud service, so that the artificial intelligence cloud service analyzes the dangerous behavior and the daily behavior of the identified object to obtain an analysis result; an analysis result sent by the artificial intelligence cloud service is received, and daily state information of the recognition object is counted according to the analysis result; comparing the daily state information with the user configuration information to obtain a comparison result; through combination of the intelligent camera and AI behavior analysis, abnormal or high-risk behaviors of the elderly can be fed back in real time and pushed to family members in time for first-time processing, so that the family members can comprehensively master the living state of the elderly, potential health risks or living abnormalities can be found in time, and the user experience is improved.