121 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 RGBT target tracking method based on convolution attention fusion, and belongs to the technical field of computer vision, and the method comprises the following steps: 1, video preprocessing; step 2, feature extraction: encoding the input image information into a feature sequence through linear mapping and a Transform encoder; step 3, local feature enhancement and fusion: selecting a local region through a sliding window, and performing cross attention calculation on the local feature of the current modal and the global feature of another modal so as to realize local feature enhancement and effectively fuse cross-modal information; and step 4, target tracking prediction. According to the method, more stable RGBT target tracking is realized through full extraction and fusion of local feature information, and the performance of the method on a plurality of video tracking data sets is superior to that of other advanced RGBT tracking algorithms.

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.

A single-target tracking method and device based on a state space model and attention and a medium are characterized in that firstly, hierarchical feature extraction is carried out, hierarchical modeling is carried out on a template and a search area through a mixed attention mechanism and the state space model, and a time sequence token is introduced into each frame at the final stage of hierarchical feature extraction to carry out target information aggregation; and aggregating the time sequence tokens by using a state space model to realize cross-frame information propagation, multiplying the obtained time sequence tokens by corresponding frame feature maps to serve as feature enhancement, and finally predicting a bounding box of a target object by a prediction head to realize target representation. According to the method, the state space model and the attention mechanism are combined for single-target tracking, especially target tracking under a large-resolution video, the state space model is introduced into the field of single-target tracking, the advantage of linear complexity of the state space model is brought into full play, meanwhile, the disadvantage of limited retrieval capacity of the state space model is made up, and high-resolution video tracking is achieved. And finally, the consumption of computing resources is reduced while accurate tracking is realized.

The invention discloses a ship intelligent cargo tracking and monitoring method and system, and the method comprises the steps: building a ship intelligent cargo tracking and monitoring frame based on a GPS positioning system and an Internet system, building a cargo tracking and monitoring APP, registering and building account information, registering cargoes, receiving and verifying the cargoes, and tracking the cargoes. Real-time monitoring and cargo inquiry are carried out on ship cargo transportation conditions; a consigner inquires cargo transportation information and receives the cargo through a cargo tracking and monitoring APP system; the cargo arrives at a transportation destination and receives cargo information with a consignee; and the consignee confirms cargo receiving and carries out transportation evaluation. According to the intelligent ship cargo tracking and monitoring method, actual transportation of cargoes is known through GPS positioning tracking, temperature tracking detection, humidity tracking detection, video tracking detection, vibration tracking detection, inclination tracking detection, pressure tracking detection and time tracking detection of ship cargoes.

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 discloses a video tracking control method and device for a non-linear moving target based on adaptive sliding mode control, which are suitable for accurate tracking of a dynamic target. The system is composed of a visual sensing module, a main control module and an execution mechanism, the visual sensing module collects position information of a target object in real time and transmits the position information to a main control chip through serial port communication, the main control chip calculates the error between the target position and the current position of the system and the error change rate, and a sliding mode surface is designed based on sliding mode control. Meanwhile, sliding mode control parameters are dynamically adjusted in combination with a self-adaptive control algorithm, so that the robustness of the system to uncertainty and external interference is enhanced, and meanwhile the chattering phenomenon is restrained. A stepping motor control signal is optimized by introducing a dynamic acceleration and deceleration algorithm, so that control smoothness and response precision are ensured, and real-time accurate tracking of a target object is realized. The method has strong robustness, high dynamic performance and control precision on the premise of not depending on a target kinematic model, can be widely applied to the fields of unmanned aerial vehicle tracking, intelligent monitoring, robot vision and the like, and effectively solves the problems of easy buffeting, response hysteresis, insufficient control precision and the like during nonlinear target tracking in the prior art.

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 visible light and infrared target tracking method and device based on a modal competition and cooperation network. The method includes: generating a first candidate sample set centered on the predicted position of the target in the previous frame of the current frame to be tracked; inputting the first candidate sample set into a trained tracking model to obtain the target tracking result of the current frame; the tracking model includes a feature extraction network and a target classifier; the feature extraction network includes a first extraction network, a second extraction network, and a weight allocation network. Both the first extraction network and the second extraction network include the first three convolutional layers of VGG-M, and each convolutional layer contains N convolutional kernels of the same size to form a convolutional kernel group. By introducing the convolutional kernel group and multiple weight allocation networks to generate a multi-core dynamic cooperative convolutional kernel, and using several convolutional kernels included in the convolutional kernel group, more diverse features can be extracted, achieving a good multi-modal video tracking effect.

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 invention discloses a target intelligent tracking method, device and equipment based on multifunctional loop fusion, a storage medium and an unmanned aerial vehicle, and the method comprises the steps: determining image motion feature data of a motion process according to a tracking video collected by a video tracking device of the unmanned aerial vehicle for the motion process of a target object, determining signal motion characteristic data of the motion process according to a tracking signal acquired by a signal tracking device of the unmanned aerial vehicle for the motion process; determining adjustment motion feature data of the motion process through reciprocal enhancement of the signal motion feature data to the image motion feature data; and controlling the flight state of the unmanned aerial vehicle according to iterative reasoning of the adjustment motion feature data and the signal motion feature data in the annular attractor network. Through information fusion of a reciprocal enhancement technology and an annular attractor network, accurate target tracking in a high-speed motion scene is realized, and the problems of high delay and large error when an unmanned aerial vehicle tracks a target object are solved.

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 present invention discloses a target-guided cross-camera continuous video tracking and monitoring method, comprising the following steps: obtaining target information, obtaining camera resource capability information of the target area and sensitivity information of the monitoring area; obtaining user priority information and monitoring task importance information; calculating, based on the target information and the camera resource capability information, alternative cameras capable of monitoring the target; calculating the monitoring effect of each alternative camera and the priority number of each alternative camera; and selecting and switching cameras for target tracking based on the monitoring effect and priority number of the alternative camera. The present invention adopts dynamic resource scheduling, incorporates user priority, the importance of the task performed by the camera, the sensitivity of the monitoring area, and the capability of the camera resource into resource scheduling, performs resource scheduling based on the monitoring effect of the camera, dynamically switches cameras, and economically realizes multi-target cross-camera continuous video tracking and monitoring.

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 a particle-based real scene multi-medium interactive simulation method, and belongs to the technical field of computer graphics and physical simulation, and the method comprises the following steps: S1, collecting an RGB image of a target scene; s2, obtaining a three-dimensional scene represented by particles; s3, inputting the acquired RGB image into a video tracking model DEEVA to obtain a multi-view mask with id; s4, rendering the three-dimensional scene represented by the original particles into a surface depth map through an expanded Gaussian renderer; s5, through the 2D id information and the depth information obtained in the S3 and the S4, back-projecting the 2D id to the particles, and distributing an id for each particle; s6, distributing physical materials for all objects in the scene, and customizing specific material attributes according to the properties of the objects; and S7, performing multi-medium physical simulation of a real scene. According to the invention, the method can cover most scenes for physical simulation, and has high versatility and high usability.

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.

The present invention discloses a method for identifying the tip of a wind turbine blade by an airborne edge computing platform of an unmanned aerial vehicle, which relates to the technical field of maintenance of wind power equipment. The method includes the following steps: Step 1, install a flexible copper mesh composed of four brackets on the top of the unmanned aerial vehicle. Conductors are led out from the connection between the brackets and the unmanned aerial vehicle. During the flight of the unmanned aerial vehicle, it is suspended to the ground by a tethered connection and connected to one end of a direct resistance meter. The present invention realizes high-precision identification of the tip of the wind turbine blade by integrating high-precision sensors and a deep learning model. The multi-layer structure of the deep learning model ensures that the model can process complex and variable blade images, improving the accuracy of identification. At the same time, the video tracking algorithm uses gradient information and a pixel point weight matrix to achieve high-dynamic and low-latency tracking of the tip, ensuring real-time performance. It not only improves the identification accuracy but also shortens the response time, providing reliable technical support for the monitoring and maintenance of wind turbine blades.

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.