79 results about "Camera network" patented technology

The invention provides a fire early warning and intelligent fire extinguishing method based on image recognition. The fire early warning and intelligent fire extinguishing method comprises the steps that a camera network is used for covering a target monitoring area, flame and smoke characteristic parameters are input, noise is removed through image preprocessing, and image data are standardized. A heat source point is selected as a camera installation position in combination with a fire propagation mode, and layout is optimized. And calling image data, performing real-time analysis based on a dynamic flame sequence, extracting abnormal response, calculating a fire risk value by using a mode recognition algorithm, and generating peak fire probability data. And collecting real-time fire characteristic data, comparing the data with a risk value after filtering and noise reduction, and setting multi-level threshold values to generate an alarm result. And analyzing a false alarm reason, optimizing the position of the camera and the starting condition of the fire extinguishing device, and generating a fire risk and fire extinguishing efficiency report. According to the invention, the accuracy and timeliness of fire early warning can be improved, the false alarm rate is reduced, and the fire extinguishing efficiency is enhanced.

The invention relates to a tunnel three-dimensional holographic splicing method and device for panoramic array camera networking, and the method comprises the steps: enabling a reference transmission camera array and a three-dimensional laser scanner on an inspection vehicle to move to different inspection positions along with the inspection vehicle for adjustment, achieving the transmission of peripheral control information at two ends of a tunnel into a measurement region, and constructing a dynamic control field; the limitation on measurement conditions is reduced; high-precision positioning and attitude determination are carried out based on fixed connection constraints of a look-around camera array and a three-dimensional laser scanner and a constructed dynamic control field, so that a measurement platform is unified to a global coordinate system, and the inherent defect of large accumulative error of a traditional three-dimensional modeling method is effectively avoided; and then the high-resolution look-around image is mapped to the three-dimensional point cloud data under the global coordinate system, so that high-resolution and high-precision three-dimensional holographic splicing of the tunnel ring surface under the global coordinate system is realized, global and local high-resolution and high-precision are achieved, and the acquisition precision of the tunnel three-dimensional hologram is greatly improved.

The invention discloses a processing method for capturing an object image in real time based on a computer vision technology, and relates to the technical field of computer vision, and the method comprises the following steps: 1, capturing an object image in real time through a USB camera and a network camera, and transmitting image data to a server side in real time through TCP / IP for processing; step 2, removing noise by using a Gaussian filter, enhancing contrast by histogram equalization, extracting image features through a hybrid architecture based on Inception V3 and ResNet-50, optimizing feature extraction through a multi-scale attention mechanism by the hybrid architecture, performing pixel-level segmentation by using a full convolutional network, dividing the image into different object areas, and performing image segmentation on the object areas; post-processing is carried out on the segmentation result; according to the method, the Gaussian filter and the histogram equalization technology are adopted, image noise is effectively removed, the contrast ratio is enhanced, image features are extracted through the Inception network, pixel-level segmentation is carried out in combination with the full convolutional network, and high-precision and high-efficiency object segmentation is achieved.

The invention discloses an unmanned aerial vehicle identification and tracking system, which can integrate multi-source visual information by calling to-be-identified images shot by a plurality of camera units, thereby determining accurate position information of a target unmanned aerial vehicle. The plurality of camera units can be monitoring camera networks which are arranged all over, the camera networks are arranged and execute tasks such as public security monitoring at ordinary times, and unmanned aerial vehicle identification and tracking can be executed, so that the hardware use cost is reduced, and the camera units can be quickly spread and implemented under the existing hardware condition; the visual field range of the camera shooting unit is matched with the low-altitude flight height of the target unmanned aerial vehicle, the blind area of the flyer detection radar can be effectively covered, and the target unmanned aerial vehicle can still be effectively recognized and tracked under the condition that the target unmanned aerial vehicle flies at a low altitude and the flyer detection radar cannot effectively detect. Supervision of the unmanned aerial vehicle is improved, and use safety of the unmanned aerial vehicle is guaranteed. The method is widely applied to the technical field of unmanned aerial vehicle management.

The invention discloses a distributed real-time target detection tracking method and system based on dynamic perception and adaptive regulation and control, and belongs to the technical field of intelligent monitoring and computer vision. The method comprises the following steps: firstly, calculating the weight of each camera in real time by using the time, position and map information of an event and camera network topology, generating an optimal search path by predicting a target motion track, and decomposing a global tracking task into multiple levels of subtasks; then, according to the task complexity and the node resource state, adopting a hybrid scheduling strategy to realize short-term container elastic expansion and contraction and long-term resource prediction, and ensuring efficient and stable resource scheduling; then, through multi-modal feature fusion and spatio-temporal joint modeling, cross-camera video data alignment, target association and continuous tracking are realized; and finally, performing multi-source data fusion and risk reasoning by using the large model, and outputting graded early warning and three-dimensional visual decision information. According to the method, the tracking accuracy can be improved, and resource utilization optimization and real-time response are realized.

An example embodiment is a system of network-capable cameras and / or sensors in vehicles in combination with a network that communicates and manages data from multiple vehicle's cameras and / or sensors to provide a comprehensive surveillance network. In some embodiments vehicle sensors and / or cameras communicate anonymously with electronic devices in range of short-range wireless communication protocols to relay the information to the network. Vehicles may be occupied or unoccupied, moving or parked, powered or unpowered. This dynamic surveillance network aids authorities and safety personnel in tracking vehicles and individuals in real-time. In some embodiments vehicle cameras and / or sensors are equipped with license plate reader technology and facial recognition technology.

A crime center system generating a graphical user interface, which may be map-based, that facilitates efficient following of a moving object across cameras covering a geographic area. The system processes information on a network of cameras to determine the locations the cameras and their orientations. An object-tracking interface is generated by the system that includes an active camera view window presenting video from a camera capturing video of the geographic area that includes the tracked object. The system determines a set of the cameras, based on known locations and orientations, capturing images about the geographic space captured by the active camera. The system generates a “halo” of camera icons in the object-tracking interface around the periphery of the active camera view window, which provides a 360-degree view of available cameras about the active camera's location allowing a user to select a next active camera to efficiently track an object.

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 provides a distributed security camera network data secure transmission method, which relates to the technical field of data transmission, realizes dynamic security evaluation of nodes in a camera network by constructing a two-dimensional dynamic confusion matrix, and realizes the security of the nodes in the camera network by combining a key fragmentation strategy and a hop distance discontinuous path scheduling mechanism. And the anti-attack capability and the data confidentiality of the security and protection video data in the transmission process are greatly improved. The main key is split and asynchronously and dispersedly transmitted, so that the risk that the key is intercepted or tampered is effectively reduced; and the data path designed by using the maximum hop distance difference strategy effectively solves the problems of predictability and attack track traceability caused by traditional path continuity. Meanwhile, the integrity and source traceability of the data fragments are ensured through a path closed-loop judgment mechanism at a receiving end, and the safety verification and recombination accuracy of the data at the link level is fundamentally ensured.

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 a fire alarm method combined with multi-camera cooperation, and relates to the technical field of fire alarm, and the method comprises the steps: obtaining a camera network of a target area, constructing a multi-Agent agent applied to fire alarm management, assembling the multi-Agent agent in each network node in the camera network in an embedded manner, determining an alarm condition based on fire characteristics, and carrying out the fire alarm management. A lightweight fire alarm module is constructed and is arranged in an alarm response Agent, a multi-Agent agent is assisted, a camera network is controlled to monitor a target area, a target point with fire characteristics is positioned, and fire tracking alarm management is executed, so that the technical problem of insufficient supervision decision ability and monitoring cooperation ability in the prior art is solved; the technical problem that the fire monitoring requirement in a complex environment cannot be met due to insufficient accuracy and timeliness of disaster alarm is solved, and the accuracy and response efficiency of fire monitoring are remarkably improved through cooperative monitoring and dynamic fire tracking.

The invention provides a computer image processing system with an image enhancement function, and belongs to the technical field of monitoring image processing. The computer image processing system with the image enhancement function comprises a multi-perception camera network module, an edge computer processing module, an image enhancement module, an intelligent video analysis module, an alarm module, a storage module, an image transmission module and a visual display module. The edge computer processing module is arranged, image processing is dispersed to the edge computer processing module, the burden of a background system is relieved, the real-time processing capacity is improved, the image quality can be optimized in real time for each monitoring scene, the video transmission strategy can be dynamically adjusted according to the image content, and the video transmission efficiency is improved. Therefore, the optimal image quality and smoothness are ensured.

The present invention relates to the field of robot obstacle avoidance technology, and specifically discloses a robot navigation obstacle avoidance method and device, the method comprising accessing a camera network of a navigation scene, periodically acquiring images from all cameras, identifying the images, locating obstacle outlines, and constructing a scene map based on the obstacle outlines; arranging the scene maps in chronological order, and extending the obstacle outlines in the scene map at the latest moment; determining the robot's motion path based on the extended scene map, and sending the extended path to the robot as navigation information; determining the application frequency of the robot's obstacle avoidance module based on the extended scene map, and sending the extended path to the robot. The present invention is used in the field of robot control with a limited scene, predetermining the obstacle position based on the camera network, and determining a navigation message that is highly likely not to pass through the obstacle based on the obstacle position. At the same time, the application frequency of the obstacle avoidance module is adjusted based on the determined obstacle position, effectively reducing resource consumption.

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.

Example implementations include a method, apparatus, and computer-readable medium for sharing analytical resources in a camera network, comprising capturing a video clip comprising a set of image frames via a first camera, wherein the first camera is part of the camera network comprising a plurality of cameras. The implementations further include transmitting, via the first camera, the video clip to a second camera in the camera network for analysis, wherein the second camera is configured to analyze, for a given period of time, one or more frames of the video clip in place of one or more frames that are locally captured by the second camera that would have been analyzed during the given period of time. The implementations further include receiving, by the first camera from the second camera, results of the analysis. The implementations include generating, for display on a user interface, the video clip with the results.

The invention discloses a continuous zooming method for a double-camera network camera based on a three-dimensional reconstruction technology, and belongs to the technical field of cameras, and the method comprises the steps: collecting double-camera image data of the double-camera network camera, and carrying out the preprocessing of the data; performing feature extraction and matching on the double-shooting image data of the double-shooting network camera, and determining corresponding points in the double-shooting image data of the double-shooting network camera; parallax is calculated according to the corresponding points, depth information of each pixel in the scene is calculated according to the parallax, and a depth map is constructed; generating a scene three-dimensional model according to the depth map and internal and external parameters of the double-camera network camera; and simulating image effects under different focal lengths based on the scene three-dimensional model so as to realize continuous zooming. According to the invention, the problems that the image shooting effect is poor and the user experience feeling is reduced due to the fact that the existing double-camera network camera cannot be subjected to better continuous zooming are solved. According to the invention, continuous zooming can be carried out on the double-camera network camera, the image shooting effect of the double-camera network camera is improved, and the user experience can be improved.

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 invention discloses a steel box girder bottom damage identification and synchronous positioning method based on a distributed camera network. Comprising the following steps: S1, forming a series of imaging equipment units according to a spatially parallel structure, and connecting the imaging equipment units together through Bluetooth and a wireless area network so as to perform image data acquisition on any position of the bottom of a steel box girder; s2, transversely dividing the bottom area of the steel box girder according to the shooting coverage range of each imaging equipment unit; s3, performing two-stage synchronous identification and positioning based on deep learning driving, in the first stage, performing lightweight reconstruction on a panoramic image by taking MobilenetV4 as a key feature extraction tool, and performing disease area identification and positioning on a global level; and in the second stage, a YOLOv9 target detection framework is utilized to analyze a disease area, and disease information is provided in a local level. The design of the invention can be widely applied to inspection of the bottom of a long-span bridge and efficient analysis of diseases.

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 invention discloses a self-supervised depth estimation network training method and system and a storage medium, and the method comprises the steps: collecting a video frame image as a training sample, and dividing the training sample into a target view and a source view; the target view is used as the input of a depth estimation network and a semantic segmentation network, the source view is used as the input of a camera network for training, the obtained trained depth estimation network is used for depth map estimation, and the depth estimation network comprises a first encoder and a first decoder; the semantic segmentation network comprises a second encoder and a second decoder; a total loss function adopted during training comprises feature vector alignment loss, vertical loss, luminosity loss and semantic segmentation loss, and the feature vector alignment loss is used for performing direction alignment on feature maps output by the two encoders; the vertical loss is used to cause the depth map output by the first decoder to satisfy vertical consistency based on the semantic segmentation map output by the second decoder.

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.