57 results about "Video tracking" patented technology

The invention discloses an in-situ global foreign matter checking and grabbing system for reactor internals on the lower portion of a reactor. The in-situ global foreign matter checking and grabbing system for the reactor internals on the lower portion of the reactor comprises a base device, an adjusting device and a foreign matter grabbing device. The foreign matter grabbing device is arranged on the base device through the adjusting device. The foreign matter grabbing device comprises a flexible arm assembly, a flexible arm driving assembly, a quick change assembly, at least one clamping jaw used for grabbing foreign matter and a video tracking and checking assembly used for searching the foreign matter and monitoring the grabbing condition. The flexible arm assembly is arranged on the adjusting device; the at least one clamping jaw is detachably arranged on the flexible arm assembly through the quick-change assembly; the video tracking and checking assembly is arranged on the adjusting device; according to the invention, the flexible arm assembly carries various types of clamping jaws to carry out automatic grabbing operation, so that the in-service nuclear power plant reactor internals can accurately, efficiently and safely grab and clean foreign matters in the in-situ interior of a pressure vessel in a whole region.

The invention discloses a multi-granularity video target tracking method and device based on a unified architecture and a medium, and the method comprises the steps: firstly designing a task specific prompt and a unified decoder, and unifying target state expressions of different granularities of masks, bounding boxes and key points through a task adaptive memory mechanism; secondly, constructing a multi-granularity video tracking data set, and fusing a memory matching core principle to realize compatibility of a single model to a multi-granularity tracking task; and finally, based on large-scale video data training, enabling the model to synchronously optimize the cross-granularity tracking capability. The invention provides a unified tracking architecture, solves the problems of insufficient generalization and parameter redundancy caused by task module independence in the existing method, dynamically adapts to feature expression requirements of targets with different granularities through a task adaptive memory mechanism, and realizes full-granularity continuous frame tracking by taking an initial state as input. And a model does not need to be independently designed for each task. According to the method, an efficient solution is provided for cross-granularity video analysis, and meanwhile standardized development of a general target tracking technology is promoted.

The invention relates to the technical field of intelligent linkage control systems for video monitoring, access control and fire alarm, particularly discloses an intelligent linkage control system for video monitoring, access control and fire alarm, and aims to solve the problems of low integration level, linkage logic solidification and high misjudgment rate of an existing security and protection system. The system comprises a multi-source data sensing fusion module, a fire situation intelligent evaluation module, a dynamic evacuation path planning module and a multi-system cooperative execution control module, and intelligent evaluation and dynamic evacuation path planning of the fire situation are realized by fusing video, access control and fire alarm data. And video tracking, entrance guard opening and closing and fire-fighting broadcasting are cooperatively controlled, so that the accuracy and efficiency of emergency response are improved.

To provide a balance function management system for balance function state information generation and a balance function rehabilitation program.SOLUTION: A memory configured to store at least one processor and at least one artificial neural network model that stores instructions executable by the processor and is executed on a computing device, wherein the at least one processor is configured to input frame images of n (where n is a natural number) videos obtained by photographing a subject through n cameras to the at least one artificial neural network model, At least one of information related to the coordinates of the head, the coordinates of the center of the pupil, and the phase change of the eyeball of the subject according to the order of the frame images of the m-th (m is a natural number from 1 to n) moving image may be acquired, and information related to the movement of the head and the movement of the eyeball for generating the balance state information or performing the balance rehabilitation program may be generated using the acquired information.SELECTED DRAWING: Figure 1

This invention discloses a video tracking and recognition method for the spatial trajectory of hoisted objects based on multi-source data fusion, belonging to the field of machine vision monitoring technology. The method includes acquiring pre-loaded physical boundary constants of the tower crane and simultaneously collecting multi-source data during tower crane operation. Based on the physical boundary constants, kinematic calculations are performed on the multi-source data to obtain the relative vertical displacement, the three-dimensional absolute spatial coordinates of the luffing trolley center point, and the absolute coordinates and three-dimensional rotation matrix of the camera's optical center in the global three-dimensional coordinate system. Edge detection is performed on the temporally aligned color video frame matrix to extract candidate wire rope contours and generate a binary edge pixel matrix. Collinear edges are mapped to polar coordinate space using Hough transform. This invention reduces the uncertainty of monocular vision depth scale and improves stability and accuracy under conditions of large-amplitude lifting and complex swinging.

The invention provides a multi-species wild animal video tracking method based on deep learning, and belongs to the technical field of wild animal recognition. The problem that tracking task modeling is feature matching between a template and a current frame image is solved, a motion model does not need to be preset, and the method can more flexibly adapt to field scenes with irregular motion and complex environment. The system is mainly composed of a feature extraction and encoder module, a detection branch, a tracking branch, an ID matching and updating module, a trajectory reconstruction module, a tracking trajectory compensation detection mechanism, a triple association strategy and a trajectory unification strategy, by adopting the tracking method based on time sequence correlation modeling, the interference problem of a complex environment and an animal movement mode in wild animal tracking can be effectively solved, and the method is more suitable for target tracking in a field dynamic scene.

The application provides an accident responsibility intelligent judgment method and system fusing video tracking, comprising: S1 aligning video with IMU, GPS and OBD for evidence collection, adopting frame hashing, chain hashing and timestamp to form an evidence chain; S2 completing detection segmentation on a shared main branch network; S3 realizing cross-frame association by combining appearance re-identification and motion model, and outputting trajectory, speed and occlusion recovery; S4 mapping the trajectory to BEV according to depth and calibration, and constructing a space-time scene graph by fusing lanes, signals, speed limits and the like; S5 extracting events such as lane changing, merging and signal violation, and positioning conflict points and yielding relationships; S6 generating fault factors according to a rule base and priority; S7 quantifying responsibility proportion according to weight, collision participation and reaction time; and S8 generating a report for storing evidence of key frames and rule lists. The system comprises collection and evidence collection, video understanding and tracking, space-time reconstruction, event extraction, rule reasoning, responsibility quantification and report storage modules.

This application provides a method, apparatus, medium, and device for video tracking of the goods circulation process. The method includes: acquiring the barcode scanning time of the goods; distributing tasks to a controller, so that the controller controls the circulation of the goods within a goods sorting system and records the time of the goods at each circulation node; capturing video of the internal workings of the goods circulation system using multiple cameras positioned at different locations; searching for videos corresponding to the goods based on the barcode scanning time and the time of each circulation node; and processing the searched videos to obtain a video tracking file of the goods. The video tracking file allows for quick and convenient visual tracking of the goods circulation system.

The invention discloses a vehicle multi-target tracking method capable of keeping a unique mark number, and belongs to the field of computer vision, aiming at the problem that the target mark number is not unique after the observation angle is changed or shielded during multi-target tracking. Vehicle videos blocked at the same observation angle or at different observation angles are collected to construct a data set, and the data set is processed according to the following process: inputting a video 1, tracking to obtain a vehicle target label and position, and intercepting a target picture frame by frame to form the data set for training a DenseNet121 network; inputting a video 2, tracking to obtain target information, inputting a target picture into the trained DenseNet121 network for re-identification, reserving an old label for an old target, endowing a new label for a new target, intercepting a new target picture to expand a data set, and updating the training network by using the data sets of the video 1 and the video 2; and inputting a video 3 and a subsequent video, and repeating the steps, namely tracking the target, re-identifying the label, expanding the data set and updating network training.

The application provides a ship water gauge video tracking method based on tide level data and related equipment, and relates to the technical field of ship water gauge measurement. In the prior art, because the installation position of a shore camera device is usually fixed and the distance between the camera device and a ship is relatively short, dynamic changes can easily cause the water gauge intersection point to move out of the field of view of a video monitoring picture, thereby destroying the continuity of video observation and making continuous measurement based on video impossible. The application ensures that the water gauge intersection point with the water edge line is always in the video monitoring picture by acquiring tide level data in real time and dynamically adjusting the focal length, pitch angle and azimuth angle of the camera device, and has the advantages of realizing continuous video monitoring, improving measurement accuracy and not needing to interrupt loading and unloading operations.

The invention relates to the technical field of highway congestion intelligent detection, and discloses a video AI technology-based highway congestion intelligent detection system, which comprises a video tracking module, a congestion analysis module and a scheduling processing module, and is characterized in that the video tracking module comprises a video data acquisition unit, an error evaluation unit and a source tracking unit; the congestion analysis module comprises a congestion analysis unit and a comprehensive evaluation unit. The scheduling processing module carries out scheduling processing on the video tracking module and the congestion analysis module. According to the system, data are collected in real time through the video tracking module, a congestion source is positioned, meanwhile, the equipment state is monitored, the congestion analysis module is combined with multi-dimensional data to generate a congestion severity coefficient, a quantitative basis is provided for decision making, and the scheduling processing module uniformly coordinates all the modules, so that the advantages of real-time response, high precision, high expandability and the like are achieved, and the system is suitable for popularization and application. The highway traffic efficiency can be obviously improved, the management cost is reduced, and the influence of congestion on the society is reduced.

A system, method, and computer-readable media for obtaining the informed consent of a patient for a medical procedure. Specifically, a video describing a medical procedure may be provided to the patient through a client device having two display portions. As the video is playing in a first portion, the client device may capture the patient watching the video and display the captured image on the second portion. The entire display of the client device may be recorded, providing a record that the patient has watched the video describing the medical procedure and consents to the medical procedure.

The application discloses a kind of high-performance video tracking methods based on 3D twin convolution network.First, a kind of space-time feature extractor based on 3D twin network is designed, for extracting the space-time feature of template sequence and search sequence.Second, a multi-template matching module is designed, by passing template feature to search feature to strengthen target feature in search block, the module includes template feature conversion submodule and space-time feature matching submodule.Template feature conversion template is used to pass appearance and motion information in template frame to search branch;Space-time feature matching module includes by two depth-related branches, respectively for classification and regression.Then, target frame prediction module is adopted, including classification, quality evaluation and regression branch, to accurately predict the target position and boundary frame of each video frame in search sequence.Finally, by minimizing the defined joint loss optimization video tracking model, for video sequence target tracking prediction.

A video tracking switching method for an electro-optical pod based on maneuvering target guidance information estimation comprises the following steps: First, during operation, the electro-optical pod calculates the estimated motion state of the moving target in the geodetic coordinate system in real time. Then, using this motion state estimate, the pitch and azimuth frame angular velocities of the inertial stabilization platform are calculated and used as feedforward values ​​for the velocity loop. Finally, the current miss deviation angle is calculated, and an adaptive prediction compensation algorithm is applied to compensate for it. Based on the miss deviation angle, an initial value compensation algorithm is used to calculate the state variable values ​​at the tracking switching moment, which are then used as the initial values ​​for the state variables of the tracking loop controller. This invention utilizes the maneuvering target's own motion state information to assist the electro-optical pod in tracking maneuvering targets during different load data switching, ensuring that the ground maneuvering target remains continuously within the effective field of view of the electro-optical load, thus effectively improving the tracking performance of the electro-optical pod.

Method for tracking a moving vehicle, wherein the method comprises the following steps: - Vehicle detection, the detection of which includes the following steps: Capturing a series of temporally related individual images, and in an initial single image, locating a reference feature that represents a vehicle; Setting up the reference feature as a full-size template; Scaling the template to full size to generate at least one scaled template, with each of the at least one scaled template being scaled by a predetermined amount; - Tracking the vehicle, the tracking of which includes the following steps: Searching a current single image for a feature that matches the full-size original, Searching the current single image for a feature that matches at least one scaled template, Set up either the full-size template or the scaled template that best matches the reference feature as the updated template; and - Repeating the tracking process using the updated template for each subsequent frame in the sequence, wherein the predetermined scaling amount is a dynamic amount that uses at least one of camera calibration data, a current position of the feature, an average vehicle speed of a current monitored traffic scene, and a scaling value from a previously tracked feature.

The present application relates to a kind of pronghorn identification system, method, processing device and storage medium based on video tracking, the system includes: data input interface, for receiving the video or image data stream from one or more camera equipment;Data type judging module is used to judge the data type of video or image data stream based on the meta information of video or image data stream;Refinement individual analysis channel is used to accurately track the individual of pronghorn in video or image data stream, trajectory optimization and behavior analysis are carried out to near view data, and the refinement individual analysis result of pronghorn is obtained;Macroscopic group analysis channel is used to carry out population statistics and rhythm analysis to long-range or aerial data in video or image data stream, and the macroscopic group analysis result of pronghorn is obtained;Data output interface is used to output the refinement individual analysis result and macroscopic group analysis result of pronghorn, the present application can be widely applied in computer vision and intelligent monitoring technical field.

The application discloses a multi-dimensional feature fusion personnel identification method and system, the method comprises the following steps: S100: a target personnel image library is established through video tracking analysis, and face features, body features and face attributes of the target personnel image library are identified to obtain a target personnel comprehensive feature library; S200: a to-be-detected image is collected through a camera; S300: face detection features, body detection features and body detection attributes of each to-be-matched personnel in the to-be-detected image are extracted; S400: similarity analysis is performed on a face base feature set and a body base feature set to obtain a face base target set and a body base target set; S500: a to-be-selected target personnel and a comprehensive score of the to-be-selected target personnel are obtained; S600: a body attribute result of the to-be-selected target personnel is obtained; and S700: an identification result is output according to the comprehensive score of the to-be-selected target personnel and the body attribute result. The personnel is identified with high precision from multiple dimensions of face features, body features and body attributes.

The application discloses a kind of steel tower vibration modal and displacement identification method of video tracking, it is related to engineering asset operation and maintenance management technical field, including the following steps: scale calibration step, based on the calibration parameter of monitoring station and camera, calculate scale calibration coefficient, provide uniform quantitative benchmark for whole link calibration;Input layer scale calibration step, mark point topology optimization layout step, space-time frequency domain interference decoupling step, multi-view heterogeneous feature federal fusion step, low-texture area vibration state reasoning step, micro-vibration signal modal identification step;Closed-loop monitoring control step, incremental model updating and data management step, by dynamically adjusting attention layer receptive field, ensure the accuracy of interference decoupling under different distances, effectively separate camera shake, wind-induced interference and steel tower itself vibration component, solve the problem that micro-vibration information is submerged;At the same time, through scale normalization and dynamic weight adjustment, realize the effective federal fusion of multi-view heterogeneous data.

This invention discloses a video tracking and early warning method for power construction sites, comprising: collecting raw video data and preprocessing it to obtain standardized video frame data; extracting high-dimensional apparent feature data using a masked autoencoder model; predicting depth information using a monocular depth estimation algorithm to generate depth feature data; fusing multimodal features, performing spatial alignment, multi-target detection and tracking to generate target tracking data; analyzing target spatial behavior, identifying abnormal behavior, and automatically generating early warning data; and storing the tracking and early warning data in a database for continuous training and optimization. This invention achieves intelligent video tracking and risk early warning for personnel and equipment at power construction sites, effectively improving the automation and precision of on-site safety management.

This invention provides an interactive video multi-target tracking and segmentation method and system, effectively solving the technical problems of existing video tracking technologies, such as the memory mechanism being mostly designed for single targets, leading to the inability to process in parallel in multi-target scenarios, and computational latency increasing linearly or superlinearly with the number of targets, making it difficult to meet real-time requirements. This invention abandons the traditional pixel-level target representation method, adopting a lightweight identity instruction with self-evolution capabilities as the core of target representation; it initializes the identity instructions of each target through user selection guidance, and uses a one-way isolation mechanism to ensure feature independence and real-time performance during multi-target tracking; simultaneously, it combines lightweight sparse memory to store the identity instructions of each target, achieving complete decoupling between inference latency and the number of tracked targets. This invention maintains a constant inference speed as the number of tracked targets increases and exhibits strong tracking robustness in complex, high-density scenarios, outperforming existing advanced video target segmentation models.

The embodiment of the application relates to the technical field of network security, in particular to a network situation assessment method and device based on video recognition, equipment and storage medium, and aims to realize overall network security situation assessment when a video tracking and recognition task is executed. The method comprises the following steps: inputting network video data into a video recognition network; performing feature recognition on the network video data through the video recognition network to obtain video recognition result data; inputting the video recognition result data into a network security situation assessment network to obtain a network security situation assessment result.

This invention provides a high-precision, stable tracking method, relating to the field of photoelectric control technology, comprising: S1: When the DIRCM acquires the initial position of the target, the position loop is controlled by a cosine trajectory planning controller to control the aiming line to reach the designated position; S2: The automatic video tracker uses full-pixel detection to output the target miss distance. After the target miss distance is compensated by a delay compensator, it is input into the coarse tracking controller to obtain the velocity loop control quantity. The velocity loop control quantity is input into the velocity loop to keep the target within the field of view; S3: The automatic video tracker uses 30% pixel detection to output the target miss distance. After the target miss distance is compensated by a delay compensator, it is input into the fine tracking controller to obtain the velocity loop control quantity. The velocity loop control quantity is input into the velocity loop to keep the target at the center of the field of view. This method includes a delay compensation strategy and a variable detector integration time control strategy, which respectively solve the problems of miss distance output delay and detector integration time.

This utility model discloses a video tracking and positioning device for ship navigation, including a monitoring camera. A mounting base is fixedly installed at the bottom of the monitoring camera, and a transparent protective cover is provided above the monitoring camera. The transparent protective cover has a hemispherical structure, and a connecting seat with an annular structure is fixedly connected to the bottom of the transparent protective cover. A connecting plate with an annular structure is fixedly installed at the bottom of the connecting seat. This utility model uses a transparent protective cover to prevent water from directly interfering with the monitoring camera. The hemispherical structure of the transparent protective cover ensures that water droplets falling on it can quickly slide off, preventing water droplets from adhering to the transparent protective cover. By coating the outer wall of the transparent protective cover with a nano-silica film, the "lotus effect" is utilized to prevent water droplets from adhering to the lens surface, reducing water film formation and ensuring that the monitoring camera can capture clear images.

The application provides a crane hook adaptive zooming and continuous tracking method, comprising the following steps: S1, multi-view field video acquisition and system initialization; S2, hook target detection and video tracking state modeling; S3, adaptive zooming decision based on pixel scale; S4, zooming modulation and focal length updating based on visual uncertainty; S5, pan-tilt linkage control and continuous centering tracking; S6, hook loss determination and re-identification based on operating state constraint. The application solves the problem of unstable tracking of traditional fixed angle or single visual feature method under long distance, small target and complex working conditions of the hook by organically integrating multi-view field video perception, adaptive zooming decision, visual uncertainty modulation, pan-tilt linkage control and re-identification mechanism based on operating state constraint, thereby significantly improving the continuity, stability and intelligent level of hook monitoring while ensuring real-time.

The invention discloses a system and method for testing the RCS of a ship in an open sea. The device comprises a mooring unmanned aerial vehicle, a bearing platform, a tested ship and a control station, the mooring unmanned aerial vehicle is connected with the bearing platform through an active cable, stable transmission of electric power and signals is achieved, and the requirement for long endurance of open seas is met. The control station is provided with a dynamic tracking module, the radar direction is adjusted through video tracking or satellite positioning guidance, and continuous alignment of the moving ship is ensured; and the calibration reflector is used for field calibration in the open sea. The testing method comprises the steps of distance range determination, open sea area screening, dynamic tracking testing, field calibration and RCS calculation, the problem that an existing offshore / shore-based test cannot simulate the real environment of the open sea is solved, the testing result has practical significance, and the requirements of ship stealth design, weapon equipment demonstration and the like can be met.

The invention discloses a basic model fine tuning method and device based on a single image pair, and the method comprises the steps: extracting a high-confidence cross-view feature point pair through VGGT, designing a lightweight adapter module which comprises a comparison convolution layer and an information theory measurement learning projection layer, and injecting three-dimensional consistency prior on the premise of keeping an original model structure unchanged. The training adopts contrast loss and Mahalanobis distance constraint, and the local discrimination and cross-view alignment capability of the features are optimized. Experiments show that the task performance of multi-view matching, pose estimation, video tracking and the like is remarkably improved on multiple data sets of ScanNet, OnePose, TAP-Vid and the like, part of indexes are improved by more than 100% compared with an original model, large-scale labeling is not needed, the calculation cost is low, and the method is suitable for scene-level and object-level tasks and has high generalization and practical value.

Method and system for identifying a predetermined event that is audibly acknowledged within content, comprising the steps of: obtaining at least a first reference data item representing a predetermine

Example implementations are for tracking an artificial reality input device by receiving, for the input device, video tracking data and inertial motion unit (“IMU”) data based on motion input. Example implementations generate, from the video tracking data, a video tracking position and a video tracking velocity; generate, from the IMU data, an IMU orientation and an IMU linear acceleration; and generate, from the IMU orientation and the IMU linear acceleration, an IMU linear velocity. Example implementations determine if the video tracking position and the video tracking velocity is reliable and determine, by a Kalman filter for the input device, a current bias, a current velocity and a current position.