87 results about "Video tracking" patented technology

The invention provides an accident liability intelligent determination method and system fused with video tracking, and the method comprises the steps: S1, carrying out the alignment evidence collection of a video, an IMU, a GPS, and an OBD, and forming an evidence chain through framing Hash, chain Hash, and a timestamp; s2, completing detection segmentation on the shared trunk multi-branch network; s3, in combination with appearance re-identification and a motion model, realizing cross-frame association, and outputting track, speed and shielding recovery; s4, mapping the trajectory to BEV according to depth and calibration, and constructing a space-time scene graph by fusing lanes, signals, speed limit and the like; s5, extracting events such as lane changing, line merging and signal rushing, and positioning a conflict point and a way giving relation; s6, generating error factors according to a rule base and priority judgment; s7, quantifying the responsibility proportion according to the weight, the collision participation time and the reaction time; and S8, generating a report evidence of the key frame and the rule list. The system is composed of an acquisition evidence obtaining module, a video understanding tracking module, a space-time reconstruction module, an event extraction module, a rule reasoning module, a responsibility quantification module and a report evidence storage module.

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 discloses a security risk intelligent response method and system. The response method comprises the following steps: acquiring video streams, equipment states and environment sensor data in real time through a multi-source interface; processing through a feature fusion engine to generate a threat matrix containing threat type judgment and confidence; matching an emergency plan library based on a dynamic weighting algorithm; an equipment control instruction queue is generated according to a matching result, conflict detection is executed, and multiple instructions of the same equipment are sequenced according to fire control, access control locking and video tracking; and finally, acquiring an equipment execution state in real time and feeding back the state to a plan library to form closed-loop control, thereby solving the problems of high false alarm rate and response lag in traditional security and protection. Historical and real-time data are fused through a dynamic decision model, the limitation of a static plan is broken through, and the disposal efficiency is improved; instruction conflicts are eliminated through a conflict resolution mechanism; and a self-healing system and model correction are combined to form double-closed-loop optimization.

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 method for establishing a network for accurate target tracking in noisy environments, comprising the following steps: setting the number of training iterations for adversarial noise training, the type and magnitude of noise generated by the noise generator, and the balance coefficient of the tracker's loss function; feeding a pair of images into the noise generator, which adds noise to the image pairs according to the set parameters, and then feeding the processed image pairs into the backbone network for feature extraction; classifying and regressing the response map obtained after feature extraction to obtain the tracking result of the current frame, and then iteratively running this process until all video frames of all video sequences in the test dataset are traversed; recording and saving the tracking result of each frame, and quantitatively analyzing the tracking accuracy and tracking success rate. This invention, while maximizing the BCE loss value, minimizes the final tracker loss value through training, thereby enabling the tracker to handle tracking in various noisy environments while maintaining tracking performance on clean videos.

The present invention discloses a video tracking method, device, terminal, and medium. The present invention combines the semantic features of the target tracking point itself with the surrounding contextual features, improving the ability to query spatial information. This allows the correct spatial features of the target tracking point to be found even in long videos or when the tracking target undergoes significant changes. Point tracking tasks in long videos are accomplished by forming a point query based on the semantic features, contextual features, and point positions of the target tracking point, and updating the point query frame by frame. The method is applicable to fields such as video editing, augmented reality, 3D reconstruction, and optical flow estimation.

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 embodiment of the application discloses a kind of normative checking method and device based on artificial intelligence, first receive and respond target detection instruction, select one or more target detection objects based on the target detection instruction, in order to better detect the operation article or operation action corresponding to target detection object, the target detection area corresponding to these target detection objects is acquired in advance, and the two are bound to form a normative check target;Image data is collected by image acquisition equipment, including static picture data and dynamic video tracking data, the data obtained after image data is preprocessed is more accurate, which helps to better extract feature points and then accurately identify targets;Embodiment greatly improves the monitoring feasibility and flexibility of student experiment process based on artificial intelligence, helps to accurately judge the operability of student experiment, and improves efficiency.

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 discloses an implementation method and device for automatic cooperation of an electronic operation ticket operation item execution process and an industrial television and a medium, and solves the problems that in the prior art, operation and monitoring are separated, the monitoring efficiency is low and information cooperation is difficult. Automatic cooperation of operation item execution and industrial television monitoring is achieved, and the system has the functions of real-time and accurate video tracking and monitoring of the electronic operation ticket operation item execution process, recording and tracing of the operation process and the like.

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.

The invention provides a high-precision video matting method and system based on target tracking, and belongs to the field of perception of computer vision. The high-precision video matting method and system based on target tracking are used for high-precision video matting. According to the method, pixel-level foreground information provided by a general video tracking framework (such as SAM2, Cutie and the like) is combined, and the trimap and alpha images are predicted frame by frame through the feature fusion network, so that accurate matting of fine-grained structures (such as hairlines and transparent boundaries) is realized, and the matting accuracy in a dynamic scene is effectively improved. The method applies but does not depend on a certain specific video tracking frame, and has good mobility and expansibility.

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 relates to the field of data processing, in particular to a real-time early warning method and system for feature recognition based on deep learning, which comprises the following steps: obtaining a matching degree group C=(c1, c2,..., c j ,...,c L ), j=1, 2,..., L corresponding to a to-be-detected feature vector; c j is the matching degree of the to-be-detected feature vector and the cluster center vector corresponding to the jth cluster; each cluster comprises a plurality of feature vectors of target object data; L is the number of preset clusters; if there is a feature vector with a matching degree greater than a preset threshold in the cluster corresponding to c1, c2,..., c q , the to-be-detected feature vector is early warned. Thus, the timeliness of early warning can be improved, and when video tracking is performed, the real-time early warning method in the application can be combined with large-scale real-time online analysis of the industrial internet, so that the timeliness of early warning based on online analysis of video tracking can be improved.

The utility model relates to tracking and aiming control equipment and a tracking and aiming system. According to the tracking and aiming control equipment, the first video tracking device and the second video tracking device are controlled by the servo control device to process multiple paths of initial guide video data and fine guide video data so as to realize accurate positioning and tracking, and a positioning and tracking result is obtained through the servo control device in combination with positioning attitude data of inertial navigation equipment. Control parameters of the control rotary table are obtained, the control rotary table is controlled to rotate, and the tracking and aiming functions are achieved; according to the technical scheme, the response speed and accuracy of tracking aiming are improved.

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.

The invention 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 a shoreside camera device is usually installed at a fixed position and is close to a ship, the dynamic change is very easy to cause a water gauge junction point to move out of a visual field range of a video monitoring picture, so that the continuity of video observation is destroyed, continuous measurement based on videos cannot be realized, and the measurement accuracy is influenced. By obtaining the tide level data in real time and dynamically adjusting the focal length, the pitch angle and the azimuth angle of the camera device, it is ensured that the junction point of the water gauge and the waterline is always located in a video monitoring picture, and the device has the advantages that continuous video monitoring is achieved, measurement precision is improved, and loading and unloading operation does not need to be interrupted.

Video tracks include positions and a classification relative to respective objects detected in a sequence of video image frames captured of a scene during a time period and the radar track includes positions and classification in relation to of an object detected in radar data captured for at least a part of the scene during the time period. An indication is obtained for each video track of the video tracks whether the radar track and the video track of the video tracks correspond based on a similarity according to a similarity measure. A second video track is ignored or deleted on condition that the radar track and a first video track correspond, the classifications associated with the radar track and the first video track correspond, the radar track and the second video track correspond, and the classifications associated with the radar track and the second video track do not correspond.

The invention relates to the technical field of airflow pattern testing, in particular to an airflow pattern analysis system and method based on a neutral buoyancy tracing device, the density of a tracing ball body is adjusted by using an inflation needle, so that the tracing ball body reaches a neutral buoyancy state in a target testing environment; multiple release modes are configured and executed according to test requirements, and release parameters are set; starting a tracer ball release assembly, and sending a tracer ball into the to-be-detected airflow according to a configured release mode; a video acquisition and analysis system is used for synchronously and continuously recording the motion trail of the tracing ball body under the action of airflow; the recorded image sequence is processed and analyzed through the video tracking unit, and a clear and continuous airflow track map is synthesized.

The application discloses a vehicle tracking method and device, which first receives monitoring data of each vehicle in a monitoring area by a spherical camera, wherein the monitoring data comprises at least one type of data in radar data, positioning data and video data; then, a target tracking mode is determined based on the type of the monitoring data; wherein the target tracking mode is a non-video tracking mode or a video tracking mode; if the target tracking mode is the non-video tracking mode, a target vehicle is determined based on the monitoring data and the number of vehicles in a target vehicle group; the monitoring data of the target vehicle is weighted and summed based on preset weights of each type of monitoring data, and a control instruction is generated based on the weighted and summed monitoring data of the target vehicle; finally, the control instruction is issued to track the target vehicle. The application can determine a corresponding tracking mode according to different types of monitoring data, expand the application range of tracking, and perform weighted sum processing on the data to make the vehicle tracking more accurate.

The invention discloses a compensation method suitable for moving target tracking image delay, and the method comprises the steps: Step 1, building a tracked target template sequence through a video tracker, namely, calculating the pixel features of a target in a tracking frame region range; step 2, the video tracker dynamically stores the graphic template with the aiming line as the center; 3, calling a graphic template stored by a video tracker according to the delay of the video stream; and Step 4, during the working period of the video tracker, generating a graphic template by using the video tracker, and dynamically storing a graphic template sequence in real time so as to be called when a moving target is captured, thereby providing an accurate template reference for continuously tracking the target. By introducing image frame delay compensation, the success rate of capturing and tracking a moving target by a photoelectric searching and tracking system is greatly improved, a man-machine efficacy mechanism is optimized, an operator can easily complete the moving target tracking process, and the market competitiveness of a product assembled with the design is improved.

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.