43 results about "Camera network" patented technology

The invention discloses an image display method for a naked-eye 3D large screen, and particularly relates to the technical field of intelligent display, which comprises the following steps of: firstly, acquiring and tracking the positions of eyes of multiple audiences in front of the screen in real time through a depth camera network, and distributing a unique ID (Identity) for each audience to realize continuous tracking; thirdly, dividing the audiences into a plurality of visual areas based on a dynamic clustering algorithm, and calculating the rendering priority of each visual area according to the number of the audiences in each visual area and the importance of the content of the concerned screen; then, left and right eye views with correct parallax are rendered in parallel for each activated visual area, and digital pre-compensation is performed by using a pre-stored optical model so as to suppress crosstalk and uneven brightness; and finally, according to the distance and angle of the visual area of the audience, querying the human factor engineering database to obtain a comfortable parallax threshold, and dynamically compressing the extreme depth of field exceeding the threshold in the scene. According to the invention, multi-audience self-adaptive high-quality 3D display is realized, and the watching experience, the resource utilization efficiency and the visual comfort are effectively improved.

The invention discloses a computer vision processing system based on machine learning, and the system comprises a dynamic topology management module which constructs and maintains a dynamic knowledge graph of a camera network in real time through a graph neural network technology, and carries out the topology self-adaption; the intelligent search module is used for dynamically selecting an optimal monitoring node for query based on a reinforcement learning technology; the multi-modal detection center is used for integrating multi-modal data, carrying out flame / smoke detection by combining an OfficientDet-D7 model with an optical flow method, integrating living body detection in a face recognition model trained in an MS1M-ArcFace data set based on an ArcFace framework to resist attacks, and carrying out multi-target tracking and cross-camera re-recognition by utilizing a DeepSORT algorithm; the distributed early warning system is used for triggering graded early warning through a dynamic threshold engine and carrying out context-aware false alarm suppression and key alarm priority response; the data persistence layer is used for storing structured data by adopting MongoDB fragmentation clusters and storing original video streams by adopting MinIO object storage; and the edge calculation node is used for locally executing lightweight calculation.

The invention provides a behavior recognition method and system based on commodity state change, a medium and equipment. The method comprises the following steps: acquiring a to-be-identified video, and performing perspective correction on each frame of to-be-identified image in the to-be-identified video to generate a standard view angle image; extracting a lattice image corresponding to each predetermined lattice area from the standard view angle image; for each lattice area, calculating a commodity state feature of the lattice area in the standard view angle image of the current frame and a commodity state feature sequence obtained in continuous frames, and calculating and outputting an event classification result of each lattice area; and performing cross-lattice consistency correction on the different lattice areas, and obtaining a behavior recognition result of the user based on a correction result. According to the method, the user behavior is identified by focusing the state change of the commodity side, the dependence on the user identity and the human body posture is avoided, and accurate monitoring can be completed without deploying a complicated multi-camera network or extra weight and RFID sensors.

The invention discloses an intelligent forest wild animal monitoring method and system based on multi-source data fusion, and relates to the field of wild animal monitoring and ecological protection, and the method constructs an integrated monitoring system by arranging a ground infrared camera network and an unmanned aerial vehicle multi-sensor inspection platform and accessing satellite remote sensing data. And the system analyzes infrared data in real time by using the edge computing node and triggers emergency response of the unmanned aerial vehicle, so that quick tracking of key events is realized. Pixel-level space-time alignment and cross-modal feature fusion are performed on multi-source image data, and a target detection model is input, so that species and behaviors can be accurately identified. Meanwhile, after the multispectral data and the geographical environment data are subjected to gridding alignment, the data are input into a habitat analysis model, and a habitat suitability probability graph is generated. And automatically generating protection decision support information through comprehensive correlation analysis of species distribution and habitat quality. According to the invention, all-weather, full-space and intelligent wild animal monitoring and protection response are realized.

This application provides a method, system, two-wheeled vehicle, and group for extrinsic parameter calibration of a two-wheeled vehicle camera network, relating to the fields of photogrammetry and calibration. The method includes: receiving marker point position and direction of travel information packets and acquiring real-shot images; extracting real-shot versions of marker points from other two-wheeled vehicles from the real-shot images; parsing the marker point position and direction of travel information packets to obtain a list of neighboring two-wheeled vehicle marker points corresponding to the real-shot images; calculating the coordinates of each projected marker point in the vehicle's coordinate system with the vehicle's camera as the origin; estimating the coordinates of the projected marker points in the pixel coordinate system; and, under conditions that meet automatic calibration requirements, determining the correspondence between the real-shot marker points and the projected marker points, and calibrating the extrinsic parameters of the vehicle's camera based on this correspondence. This method reduces dependence on external calibration objects, enabling real-time calibration in dynamic environments and improving the efficiency of automatic calibration of camera extrinsic parameters.

The application relates to the field of regional security management, and particularly discloses a big data security monitoring system, which comprises an image acquisition module, an image application module, a supply data detection module and an emergency end control module. The image acquisition module is used for inserting an information reader into a camera network to acquire scene images under dynamic frequency by the information reader. The image application module is used for carrying out numerical conversion on the scene images, and determining target distribution in a region according to the converted values. The supply data detection module is used for calculating a demand value according to the target distribution, randomly detecting supply data according to the demand value, and outputting a risk value according to a detection result. The emergency end control module is used for generating a control instruction pointing to an emergency end, and sending the control instruction to the corresponding emergency end. The application acquires images through a monitoring system, carries out contour recognition on the images, further determines energy supply parameters required by the images, identifies actual energy supply parameters according to the energy supply parameters, judges the risk value, accurately and timely generates reminding information, and reports the reminding information to senior management personnel.

The invention discloses a vision-based large-scene vehicle continuous tracking method and system. The method comprises the following steps: extracting multi-dimensional features of a target vehicle by using a feature extraction model; when the target vehicle is observed by a plurality of cameras in the monitoring camera network at the same time, generating cooperative state estimation of the target vehicle under a global coordinate system by integrating observation data of each camera and the network topology structure; on the basis of collaborative state estimation, predicting to obtain an ordered subsequent candidate camera set and respective corresponding prediction space-time windows; according to the sequence of the candidate cameras, re-identification matching is carried out based on the multi-dimensional features in the corresponding prediction space-time windows, and cross-camera relay tracking of the target vehicle is realized; and the world coordinate points of the target vehicle which is continuously and successfully tracked are spliced according to a time sequence to form a complete space-time trajectory, and the space-time trajectory and the high-precision GIS map of the wharf are displayed in an overlapped mode. According to the invention, the robustness and accuracy of vehicle tracking in a bulk cargo wharf scene can be improved.

This invention discloses a distributed multimodal UAV angle perception and prediction method, comprising the following steps: S1, determining the system model; S2, determining the visual signal and radio frequency signal models; S3, fusing visual information from multiple cameras to estimate the UAV's orientation relative to the base station at the current moment; S4, fusing the visual estimation results from historical time slots with the wireless echo perception results to predict the UAV's state at the next moment; S5, based on the UAV's state at the next moment predicted in step S4, calculating and generating the optimal transmission beam for the base station antenna in advance. This invention eliminates the blind spots of a single base station's perception through a distributed camera network, achieving omnidirectional, continuous, and robust positioning of UAVs in complex urban environments; by fusing visual and wireless echo information for high-precision motion prediction, the communication system can pre-align with the UAV, transforming passive tracking into proactive service, thereby significantly improving the stability and transmission efficiency of low-altitude communication links.

The invention discloses an intelligent fire-fighting robot system and a control method. Comprising the following steps: acquiring wide-area environment image information through a fixed monitoring camera network deployed in an operation area, and acquiring approaching environment image information through a mobile sensing system carried by a fire-fighting robot; according to the intelligent fire-fighting robot system and the control method, the fire-fighting robot can automatically adapt to complex terrains such as oil stains and stairs and accurately recognize fire source properties, so that the optimal moving mode and the fire extinguishing agent are automatically selected. According to the system, sensing data of fixed monitoring and the robot body are fused, dynamic path planning and personnel guiding are achieved, the traffic capacity, the fire extinguishing precision and the personnel rescue efficiency in a complex fire scene are remarkably improved, and the risk of rescue personnel is effectively reduced.

The invention discloses a variable-focus monitoring target recognition system based on multi-agent coordination, and the system comprises a monitoring camera network which is responsible for sensing an environment, executing an action, and collecting and outputting image data of an external park; the intelligent Agent set is in network communication with the monitoring camera and is used for identifying the received image data or controlling the camera to act; the task distribution module communicates with the intelligent Agent set, receives an external monitoring task, decomposes the external monitoring task and distributes the external monitoring task to the set; and the performance evaluation and feedback module is also in set communication with the intelligent Agent, continuously monitors and obtains key performance indexes, and provides feedback signals for Agent learning and strategy optimization. According to the system disclosed by the invention, efficient identification of the monitoring target is realized by utilizing multi-agent coordination. The modules cooperate in a division manner, the camera network provides data, the intelligent Agent set carries out processing and control, the task distribution module reasonably arranges tasks, the performance evaluation and feedback module assists in optimization, the intelligence and precision of the monitoring system are improved, and the system is suitable for various complex park monitoring scenes.

The application discloses a kind of multi dual-spectrum camera network cascading motion trajectory analysis methods, it is related to trajectory analysis technical field, for solving the technical problem of target cross lens trajectory tracking under complex scene;Through the networking cooperation of multiple dual-spectrum cameras, cross-view continuous tracking of target and motion trajectory reconstruction are realized, innovatively adopt discrete quality level to carry out the gating splicing of trajectory segment, ensure that trajectory fusion process is accurate and reliable;Conflict detection and evidence backtracking mechanism are introduced, the reliability and auditability of trajectory association are guaranteed, and commitment archiving and on-demand evidence collection process are designed to meet the actual monitoring evidence management requirements;The method can enable a reliable multi-camera cross-domain trajectory analysis system for the skilled person in the art, significantly improve the intelligence and reliability of monitoring target trajectory extraction and evidence management.

The invention discloses a vision and laser dual-redundancy anti-collision system and an anti-collision method, and relates to the technical field of crane anti-collision, the vision and laser dual-redundancy anti-collision system comprises a data acquisition layer and a data processing layer, the data acquisition layer comprises a plurality of laser radars and industrial cameras, the laser radars acquire three-dimensional point cloud data, and the industrial cameras acquire picture information of multiple visual angles; the data processing layer comprises a camera network, a laser radar network and a 3D detector, the camera network extracts a first feature from the picture information, the first feature comprises a multi-view two-dimensional feature and a multi-view inference-based three-dimensional feature, the laser radar network extracts a second feature from the point cloud data, and the second feature comprises a three-dimensional structure feature; the first feature and the second feature are fused and then input into a 3D detector, and the 3D detector identifies target information and performs system control and / or early warning based on the identified target information. The effects of accurately detecting the moving speed and the moving direction of the moving target and avoiding the obstacle are achieved.

This application relates to a zero-interaction permission recognition method and system based on facial recognition and ERP / MES integration, belonging to the field of information security recognition technology. The recognition method includes: real-time acquisition of facial images through a multi-camera network deployed in different scenarios; preprocessing the facial images to construct a facial feature relationship network, extracting facial feature vectors, and performing liveness detection; outputting identity features when the liveness detection confidence exceeds a preset confidence threshold; comparing the identity features with a pre-stored employee feature database in the ERP / MES system in real time, and binding employee identity identifiers and real-time permission data based on the comparison results; automatically triggering corresponding device automatic authorization commands based on permission binding tags matching the current scenario; and recording the execution events of the device automatic authorization commands to generate structured audit logs. This application can achieve high-precision identity verification without the need for active user cooperation, and instantly trigger corresponding levels of device control and data access permissions accordingly.

1. The name of the design product: camera (thousand eyes). 2. The use of the design product: for computer camera, network video chat. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

This utility model discloses a camera protection device for online automatic detection of the appearance quality of carbon anodes. It includes a protective shell, with a front baffle and a flange baffle fixed to its two ends respectively. The front baffle has an inner circular hole. An optical glass and a pressure plate are sequentially arranged on the outer side of the front baffle. Both the pressure plate and the front baffle have inner circular holes. An optical glass impurity removal device is installed on the protective shell above the optical glass. A square blind plate is installed on the outer side of the flange baffle. A waterproof connector for the camera network cable, a waterproof connector for the camera cable, and a compressed air hose quick-connect plug A are installed on the square blind plate. A compressed air hose quick-connect plug B is also provided on the protective shell. This utility model uses a sealed protective device with cooling function for detection operations to ensure the stability of the detection. The optical glass impurity removal device can promptly remove dust adhering to the surface of the optical glass, ensuring clear images captured by the camera, thus meeting the operational requirements of online detection of the appearance quality of carbon anodes.

The invention discloses a multi-camera network optimization selection method based on a large model. The method comprises the following steps: extracting multi-dimensional representation information of a camera network; generating a selection scheme of a camera network through step-by-step reasoning of a multi-modal large model and guidance of greedy search; the generated selection schemes are evaluated through multi-dimensional evaluation indexes, and the selection schemes meeting index requirements are output; constructing a comprehensive health degree score to perform anomaly detection on the camera network; and executing an incremental optimization strategy on the locally abnormal camera network, and solving a camera network selection adjustment scheme with the lowest cost. The method has the beneficial effects that by introducing the multi-modal large model, the system is endowed with the capability of watching scenes and understanding rules; through multi-modal scene understanding, semantic constraint reasoning, hybrid intelligent optimization and dynamic adaptation adjustment, a high-quality and interpretable camera selection scheme can be automatically generated.

The invention discloses an intelligent monitoring and evaluation system and method for prison night duty performance. The system comprises a multi-source behavior perception unit, an edge real-time analysis unit and a cloud risk cognition and evolution unit. Non-blind area sensing is realized through UWB and a multi-camera network, and the edge unit carries out real-time abnormity identification and alarm; the cloud end cooperates with a multi-LLM agent through a performance knowledge graph, carries out deep correlation analysis on the event, actively discovers a cross-space-time potential composite risk mode, and generates a comprehensive assessment report; and the system can dynamically update the knowledge base and the detection rule by absorbing new knowledge and analyzing a natural language instruction, so that self-evolution is realized. According to the invention, the problems of passive monitoring, subjective evaluation and system stiffness in the prior art are solved, and the initiative, intelligent level and continuous adaptive capacity of prison night supervision are improved.

The invention discloses a calibration method based on an image analysis measurement technology, and relates to the technical field of safety monitoring. Comprising the following steps: S1, initial calibration: setting a calibration identification point on an installation auxiliary facility of a crane, carrying out synchronous image acquisition through a camera network, and carrying out one-time global calibration according to the acquired synchronous image; s2, data resolving: acquiring a synchronous image through camera network acquisition, identifying the synchronous image through a computer vision algorithm, and determining multi-dimensional parameters; and S3, index evaluation: taking the structural state geometric parameters as the input of the constructed parameterized agent model, outputting and obtaining a corresponding comprehensive safety index, and determining a corresponding early warning signal according to the comprehensive safety index. According to the method, evaluation is carried out from multiple dimensions, the macroscopic situation can be monitored, microcosmic lesions can be observed, and then the three-dimensional and comprehensive perception of the structure safety state is achieved.

The invention discloses a personnel trajectory tracking method and system based on network topology and multi-modal features, and the method comprises the steps: constructing a physical topological graph of a monitoring camera network, and calculating a space-time transition probability weight between nodes; extracting face, clothing and gait multi-modal features of the target person by adopting a double-flow neural network; when the target disappears from the current node, predicting a candidate node set possibly appearing at the next moment by using the space-time diagram neural network according to the topological weight; pedestrian detection and feature extraction are carried out in the candidate node video streams, and the feature similarity of each modal is calculated; carrying out dynamic weighted fusion on the space-time transition probability and the multi-modal similarity through a fusion scoring function to obtain a final matching score, and judging a target position and updating a track according to the final matching score; the system also supports multi-target collaborative tracking and heterogeneous sensor data fusion. The method effectively solves the problems that a traditional method is low in recognition rate and large in calculation redundancy in complex scenes such as reloading, shielding and illumination variation.

The invention discloses an encryption and authorization system and method for a microscope trial prototype, and relates to the technical field of medical instruments. The system comprises a microscope body, an identity binding module, an authorization code generation module, an encryption module, an authorization verification module, a function control module and a manufacturer database, wherein the microscope body comprises a main control circuit board and a camera board which are communicated through a serial port. A camera network MAC address is used as a unique identity label, and binding and two-way identity verification of a machine code and the MAC address are realized; a long-term / short-term hierarchical authorization code is generated based on the MAC address and the authorization deadline, and a unique authorization file is generated through AES-128 encryption, base64 serialization and serial number verification; and multi-dimensional verification of the MAC address, the authorization deadline and the serial number is completed after startup, a core function is started if verification passes, and shutdown and prompting are carried out if verification fails. And remote monitoring, abnormal log and expiration reminding functions are newly added, so that undeadline trial and hardware replacement avoidance behaviors are effectively prevented, a trial-to-sales scene is adapted, the rights and interests of manufacturers are guaranteed, and the use experience is improved.

The invention discloses a soldier physical training evaluation platform system, and belongs to the technical field of training evaluation. Comprising a training and examination evaluation platform system and infrastructure equipment, and the training and examination evaluation platform system comprises a basic physical training and examination management subsystem, an exercise ability and exercise analysis evaluation subsystem, a personalized training guidance system, a user and authority service module, a system basic data service module and a system data interface service module. The infrastructure equipment comprises an AI camera, a network hard disk video recorder, an intelligent all-in-one machine, a body type measuring device, an examination operation terminal, a training operation terminal, an LED large screen, an algorithm server, a management server, a POE gigabit switch, a full gigabit router and a wearable device. According to the system, data can be automatically collected and processed, the training and examination efficiency is greatly improved, the officer and soldier body characteristics and potential risk points are accurately recognized through dynamic monitoring and data analysis, a personalized training plan is made according to the officer and soldier physical ability level, and the practical value is high.

The invention disclosed herein offers a system that allows users to virtually navigate between multiple, simultaneous live events occurring in disparate geographic locations. The system organizes a network of cameras into distinct groups, each tasked with capturing video streams of a specific event at a unique venue, such as a sports stadium. These video streams are captured from various angles, providing a multi-dimensional viewing experience. Users have the flexibility to manually or automatically switch between these events, thereby crafting a unique and personalized viewing experience.

The invention relates to the technical field of computer vision and three-dimensional graphics, and particularly discloses a multi-camera three-dimensional reconstruction driven target identity and track positioning method, which comprises the following steps of: acquiring a video sequence through a synchronous camera network and constructing an implicit representation model only comprising a static background structure so as to establish a stable three-dimensional coordinate system; target detection is carried out on the real-time video stream, two-dimensional coordinates of a detection point are reversely projected, and accurate three-dimensional space coordinates are calculated by querying the static model; and finally, fusing the motion state, appearance and behavior characteristics of the target to carry out multi-target tracking and identity association, and generating a three-dimensional motion track with a consistent identity label.

The invention discloses an expressway camera-crossing vehicle track reconstruction method, and relates to the technical field of intelligent traffic monitoring, and the method comprises the steps: automatically constructing a network topological relation according to the pose information of a monitoring camera; an entrance recognizes license plates, creates an initial track and adds the track to a track set; performing multi-dimensional similarity fusion on the main road based on vehicle color, type and appearance re-identification features, screening candidate trajectories in a topological constraint and time window, and determining a target trajectory to continue the path; the exit recognizes the license plate to realize track closing. And processing the identification error through a predefined color and vehicle type similarity matrix, and dynamically adjusting the time window based on the real-time vehicle speed. The method can achieve whole-course track reconstruction, improves the vehicle distinguishing capability, is suitable for a large-scale camera network, and reduces the dependence on license plate recognition.

The application discloses a camera monitoring network target matching method based on a camera network topology, which comprises the following steps: S1, collecting relevant scene data, constructing a training set according to target images in the relevant scene data, training a target matching network, and obtaining a target matching model; S2, inputting a target image to be matched into the target matching model to obtain a preliminary target matching image sequence; and S3, reordering the preliminary target matching image sequence through a feature distance measurement formula based on the camera network topology to obtain a target matching result. The application reorders the preliminary target matching image sequence output by the target matching model obtained through training by using information of the camera network topology, improves the matching accuracy, and further takes into account the performance and efficiency of the target matching method under complex conditions.

The application relates to the technical field of unmanned aerial vehicle interception, and discloses an aerial unmanned aerial vehicle autonomous interception method based on a ground camera network, which comprises the following steps: a node camera in the ground camera network monitors a no-fly area and continuously collects images; an intrusion target object in the image collected by each node camera is detected through a target detection algorithm, and the two-dimensional coordinates of the intrusion target object in the imaging plane of the node camera are calculated; the position of the intrusion target object in a three-dimensional space is solved; an interception trajectory of an interception unmanned aerial vehicle is designed; and a trajectory tracking controller of the interception unmanned aerial vehicle is designed to control the interception unmanned aerial vehicle to intercept the intrusion target object. The application can avoid the occurrence of target loss caused by the self maneuvering of the interception unmanned aerial vehicle and the escape flight of the intrusion target object, can reduce the hardware load of the interception unmanned aerial vehicle, can reduce the operation burden of the interception unmanned aerial vehicle, and can improve the continuous tracking and interception capacity of the interception unmanned aerial vehicle on the intrusion target object.

The application provides a terminal camera network extension device supporting a bypass function for mining and a method thereof.The device comprises a camera, an Ethernet exchange module, a bypass circuit, a DSL communication module, an intrinsic safety protection module and a twisted pair interface.A bypass circuit is arranged between the Ethernet exchange module and the DSL communication module, so that the DSL communication module can play a relay role, and even if power is cut off, the bypass circuit can make the DSL communication module directly connected, so that the twisted pair interface can still perform signal amplification through the DSL communication module, and the communication distance of the DSL port is not affected.

The application discloses a multi-camera network optimization selection method based on a large model, comprising the following steps: extracting multi-dimensional representation information of the camera network; generating a selection scheme of the camera network through step-by-step reasoning of a multi-modal large model and guidance to a greedy search; evaluating the generated selection scheme through multi-dimensional evaluation indexes, and outputting the selection scheme meeting the index requirements; constructing a comprehensive health degree score to detect the abnormality of the camera network; and executing an incremental optimization strategy on the locally abnormal camera network to solve the camera network selection adjustment scheme with the lowest cost. The application has the beneficial effects that: by introducing a multi-modal large model, the system is endowed with the ability to understand the scene and understand the rules; through multi-modal scene understanding, semantic constraint reasoning, hybrid intelligent optimization and dynamic adaptive adjustment, a high-quality and interpretable camera selection scheme can be automatically generated.