962 results about "Multi target tracking" patented technology

The invention relates to the technical field of multi-target tracking, in particular to a dynamic multi-target tracking and trajectory prediction method. Comprising the following steps: acquiring a color image and a depth image, and preprocessing; constructing a dynamic threshold double-flow neural network, extracting features from the preprocessed color image and depth image to obtain a visual feature set and a depth feature set, fusing the visual feature set and the depth feature set through an adaptive attention mechanism to obtain a fused feature set, and combining a dynamic threshold to obtain an observation information list of a target; updating the observation information list through a self-motion decoupling mechanism to obtain a real information list; establishing space association and time association between the target and the track based on the real information list to obtain a newest track set; and analyzing target characteristics, environmental constraints, social behaviors and historical trajectories, generating a prediction trajectory of each target and a corresponding multi-factor score, generating a time-varying influence graph, and performing planning and decision making by adopting a double-layer decision-making mechanism.

The invention discloses a construction site unsafe behavior detection method and system based on image recognition, and relates to the technical field of intelligent visual safety monitoring, and the method comprises the steps: collecting a construction image containing a constructor, carrying out the rectangular frame labeling of unsafe behaviors in the construction image, and generating a structured labeling data set; inputting the structured annotation data set into a YOLOv5 target detection model, performing training through Mosaic data enhancement and an adaptive anchor box generation algorithm, and outputting the trained YOLOv5 target detection model; based on the trained YOLOv5 target detection model, performing 128-dimensional feature vector extraction and track association matching on a detected target through a DeepSORT multi-target tracking algorithm, and outputting a track data table; and the recognition stability and behavior analysis depth of the system in a complex environment are enhanced, so that automation and intelligentization of safety management of a construction site are effectively supported.

The invention discloses a cross-border tracking traffic early warning identification method based on multi-array cameras, and the method comprises the following steps: S1, deploying the multi-array cameras, a laser radar and an auxiliary sensor, and collecting original image data; s2, preprocessing the collected original image data and the point cloud data collected by the laser radar to generate a multi-modal data set; s3, constructing an improved Deformable DETR model, performing feature extraction on the multi-modal data set by using the improved Deformable DETR model, identifying the category and the position of a target, and generating a detection frame and a confidence score; s4, according to a target detection result, constructing a multi-target tracking framework, and extracting a motion trail of the target; and S5, analyzing the movement track of the target, extracting the movement characteristics of the target, and detecting an abnormal traffic event. According to the method, the improved Deformable DETR model is combined with multi-modal data fusion, accurate detection and intelligent tracking of the cross-border target are achieved, and the method has the advantages of being high in detection accuracy, high in tracking stability and accurate in anomaly recognition.

Various examples, systems, and methods are disclosed relating to dynamic novel view reconstruction based at least in part on flow rematching. A first computing system can update a graph neural network based at least on video data representing a plurality of first objects and a plurality of first labels corresponding to the plurality of first objects. The first computing system can cause the graph neural network to generate a plurality of second labels of a first example video and update the graph neural network based at least on the plurality of second labels and the first example video. The first computing system can cause the graph neural network to generate a plurality of third labels of a second example video. The first computing system can output a request for a modification to the at least one third label responsive to the uncertainty score satisfying an annotation criterion.

The invention discloses a bird flight path intelligent tracking and monitoring method and system based on big data analysis. The method comprises the following steps: S1, generating an original bird observation data set; s2, outputting a standardized bird observation data set; s3, calling a multi-target tracking algorithm based on the standardized bird observation data set to obtain a bird trajectory fragment data set; s4, constructing a lion group optimization search model, and inputting the bird trajectory fragment data set into the lion group optimization search model to form a candidate trajectory data set; s5, inputting the candidate trajectory data set into a space-time Transform model configured with rotation position embedding, and generating a continuous flight path data set; and S6, outputting a cross-region bird migration monitoring result set. According to the method, bird migration continuous tracking requirements under different time periods, multiple geographical zones and heterogeneous equipment acquisition conditions can be covered, application scenes of ecological protection, migration research and airspace management and control are effectively supported, and the method has remarkable engineering popularization and ecological protection values.

The invention discloses a self-adaptive illegal parking detection method, a detection system and a storage medium. The self-adaptive illegal parking detection method comprises the following steps: initializing the system; each video frame in an input video stream is processed according to the following steps: vehicle detection; performing regional filtration; performing multi-target tracking; vehicle state calculation: traversing each tracked vehicle, and updating the information of the tracked vehicle in the vehicle information object; multi-dimensional illegal parking judgment and alarm: according to a calculation result in the parking duration calculation step, executing the following judgment logics: traversing all static vehicles, determining a scene, obtaining a dynamic threshold value, triggering condition judgment and triggering an action; result visualization and output are carried out; and cleaning resources. By introducing the multi-dimensional dynamic judgment logic, the technical scheme of the invention can identify illegal parking behaviors more intelligently and more accurately, and the practicability and reliability of the system are significantly improved.

The invention provides a lottery store violation detection method and system based on multi-modal data fusion, and relates to the technical field of intelligent monitoring, and the method comprises the steps: detecting a current abnormal event, carrying out time sequence perception target detection on monitoring video data, and recognizing violation electric equipment in a video image frame based on adaptive feature enhancement and multi-target tracking. The method comprises the following steps: acquiring time sequence data of current abnormity and illegal electric equipment detection, constructing a time sequence incidence relation of current abnormity, equipment detection and an electric state based on a dynamic causal network, calculating a time-varying weight and instantaneous causal intensity by utilizing conditional entropy increment, acquiring optimal time lag in combination with eigenvector conversion and a dynamic programming algorithm, and determining the current abnormity and illegal electric equipment detection according to the optimal time lag. And weighting the integral of the instantaneous causal intensity and the exponential function of the optimal time delay to obtain an event matching score, and distinguishing a temporary power utilization event and a continuous illegal power utilization event based on the event matching score.

The invention discloses a radar and vision multi-mode fused three-dimensional multi-target tracking method and related equipment. The method comprises the following steps: establishing a three-dimensional constant turning rate and speed motion model for detecting a target vehicle; constructing a multi-modal measurement model, and obtaining a fusion target measurement result according to the point cloud data and the image data collected by the 4D millimeter wave radar; performing two-stage front and back frame matching and filtering according to a fusion target measurement result to obtain a matching target of the track; track life cycle management: newly building, confirming, keeping or deleting the track according to the matching result and the time step; according to the rule after track re-tracking, under the condition that the target is temporarily lost or shielded, a virtual track in the shielding period is constructed, state correction is conducted on the virtual track, the corrected state serves as the initial state of the current moment, and tracking continues. According to the invention, through fusion of complementary information of the 4D millimeter wave radar and the visual sensor, accurate sensing and tracking of three-dimensional multiple targets in a complex automatic driving environment are realized.

The invention discloses a home decoration construction site monitoring system and method based on artificial intelligence, and relates to the technical field of video processing. A video data set, an environment parameter set and an equipment state set in a historical construction process are collected in advance, an image semantic segmentation model is constructed based on the video data set, and construction scene division is performed; constructing a target detection model based on the video data set, generating a target spatial-temporal trajectory based on a detection result of the target detection model by using a multi-target tracking algorithm, and constructing a construction behavior recognition model based on the video data set and the target spatial-temporal trajectory; based on the recognition result of the construction behavior recognition model, the environmental parameter set and the equipment state set, constructing a risk early warning model; the safety condition of a construction site is evaluated in real time, and dynamic risk early warning is provided.

The invention provides an online multi-target multi-camera vehicle tracking method based on deep learning. The method mainly comprises the following steps: capturing vehicle video images from a plurality of cameras; identifying and positioning a vehicle instance by using a YOLOv11 algorithm; 2048-dimensional appearance features of the vehicle are extracted through a ResNet101IBN convolutional neural network; applying a single-camera multi-target tracking algorithm to generate a target track under each camera; and through a hierarchical clustering algorithm, in combination with feature cosine distance and Dunn index optimization, clustering is performed on a target trajectory, and cross-camera target matching is completed. The method iteratively executes the steps, and meets the multi-target vehicle tracking requirement in a continuous video stream. By integrating a lightweight vehicle detector, efficient appearance feature extraction, accurate single-camera tracking and an advanced cross-camera association strategy, the real-time performance, accuracy and robustness of vehicle tracking are remarkably enhanced, and the method is suitable for complex multi-camera monitoring environments such as urban monitoring, traffic management and automatic driving assistance systems.

The invention provides a multi-target tracking method based on EKF-ANA and multi-distance trajectory matching, relates to the technical field of multi-target tracking, and provides an adaptive extended Kalman filter algorithm for a trajectory prediction stage and a multi-distance trajectory matching method for a trajectory matching stage based on a two-stage multi-target tracking framework. And a multi-target tracking function under a complex background is realized. According to the method, a two-stage detection-based multi-target tracking mode is used, firstly, the position, the category and the confidence coefficient of a detected target are obtained through a detection algorithm, and then a track is predicted through a self-adaptive extended Kalman filtering algorithm. And finally, performing association operation between the target and the track by using a Hungary matching algorithm based on a multi-distance weighted data association cost matrix. According to the method, the accuracy of target tracking can be improved, the problem of partial ID switching caused by shielding is reduced, and certain real-time performance is met while a multi-target tracking task is completed.

The invention relates to the technical field of self-service, and particularly discloses a non-inductive payment method and device, and the method comprises the steps: collecting goods taking pictures of a plurality of clients through a binocular camera and an infrared array which are disposed at the top and side surfaces of a cabinet body, extracting hand key points through posture estimation, and distinguishing the identity through multi-target tracking; a gravity sensor under a shelf board detects the weight change of each goods allocation, and combines with a visual track to construct the space-time correlation of goods movement, so as to realize the cross validation of goods taking behaviors; the convolutional neural network performs abnormal behavior detection on the multi-modal data and generates a risk score; creating a virtual shopping basket containing a space-time mark for each customer, and judging commodity attribution by adopting a decision tree algorithm; and executing distributed bill generation and multi-channel fee deduction according to the risk level, and completing real-time settlement in a multi-client concurrent scene. According to the method, the commodity affiliation accuracy and the payment security are remarkably improved, and efficient and stable unmanned retail operation can be realized.

The invention relates to the field of tunnel fire prediction, in particular to a new energy vehicle fire early warning method and system in a tunnel. The method comprises the following steps: identifying a new energy vehicle entering a tunnel by using a YOLOv8 model, and obtaining a visible light image, a thermal imaging image and millimeter wave radar point cloud data of the new energy vehicle; a multi-target tracking algorithm is introduced, target tracking is conducted on the new energy vehicles, the battery temperature of each new energy vehicle is calculated in combination with tunnel environment data, space-time synchronous calibration is conducted on collected data, and then the motion trails of the new energy vehicles are calculated; time synchronization calibration is conducted on the movement track and the battery temperature, and the battery temperature gradient of the new energy vehicle is calculated according to the result obtained after time synchronization calibration; and predicting and alarming the tunnel fire according to the battery temperature gradient. The problem that in the prior art, fire early warning for the new energy vehicle in the tunnel is lacked is solved, and tunnel operation safety is improved.

The invention discloses a multi-target tracking method combining camera motion compensation and pseudo depth estimation, belongs to the field of computer vision, and is suitable for a complex automatic driving road environment. The method comprises the following steps: constructing a training set and a test set; detecting the image by using a deep learning detector and extracting features; a Kalman filter is adopted to correct a motion modeling state vector, and the target position and size prediction precision is improved; solving a homography matrix through feature point matching, performing global camera motion compensation, and reducing camera jitter and displacement interference; target pseudo depth information is calculated, hierarchical cascade matching is carried out, and association performance in dense and shielding scenes is optimized; a three-level cascade strategy is adopted to complete high confidence degree, low confidence degree and residual target matching in sequence; and finally, outputting a tracking result with a detection frame and identity information to obtain a trained model. According to the invention, accurate detection and stable tracking of multi-category targets can be realized in a complex environment, and identity switching is effectively reduced.

The invention belongs to the technical field of target tracking, and discloses a multi-target tracking method based on long and short term trajectory association. The method aims at solving the problems of precision limitation, track breakage, frequent identity switching and the like caused by insufficient collaborative optimization of detection and tracking tasks in a complex scene and limited long-term track modeling capability in an existing method. The invention provides a multi-target tracking method based on long and short term trajectory association. The method comprises the following steps: generating a target detection frame and detection query through a detection module based on YOLOX; the MOTR-ConvNext network dynamically updates the track query and predicts the target motion information; a track query memory module is used for storing and updating a historical track; the detection frame and the prediction frame are subjected to IOU matching through a matching module, and short-term association is achieved; cross-frame correlation is calculated for unmatched detection through a history backtracking module, and long-term correlation is achieved; and finally, combining the two association results to obtain a complete tracking result. According to the method, the tracking precision can be remarkably improved in a complex scene, and higher robustness is achieved in the aspect of target identity maintenance.

The invention discloses an airborne radar ground moving target stable tracking method, and belongs to the technical field of avionics, and the method comprises the following steps: S1, obtaining and fusing multi-modal measurement data; s2, clutter suppression and pretreatment; s3, target state estimation; s4, data association and track repair; s5, group target collaborative tracking; and S6, outputting a tracking result. According to the airborne radar ground moving target stable tracking method, multi-mode fusion and track adhesion are used for supplementing a single measurement short plate and repairing a broken track; by means of clutter map and optimization filtering strong clutter suppression, the state precision is improved; the multi-target tracking is optimized by using the graph model and the group target modeling, the complexity is reduced, the prior is fused, and the tracking stability of the complex scene is enhanced.

The invention relates to the technical field of computer vision and multi-sensor data fusion, in particular to a multi-sensor image fusion obstacle real-time detection and tracking system, which comprises the following steps of: firstly, fusing data of a camera, a millimeter wave radar and a laser radar, and extracting and fusing multi-modal features; performing multi-target tracking based on a recurrent neural network: generating a target state through space-time modeling, associating a target with a historical track by using an attention mechanism, and maintaining track consistency; and the system performs semantic classification and interaction analysis on the obstacle, predicts the movement track of the obstacle, realizes deep semantic understanding, and finally outputs the identity label and the complete historical track of the obstacle.

The invention relates to a multi-modal data fusion-based multi-target tracking method for a legged robot. The method comprises the following steps of S1, data acquisition and preprocessing; s2, extracting data features; s3, multi-channel feature fusion extraction: based on the high-dimensional feature representation obtained in the S2, fusing the data features of the data of different sensor channels to obtain a multi-modal feature map, and processing the multi-modal feature map through a multi-task detection head to obtain a target detection result; s4, multi-target tracking: fusing data features of different sensor channels by using a feature sharing module, and outputting the fused data features to a multi-target tracking module; and the multi-target tracking module predicts the motion trail of the target in combination with the state of the target in the previous frame. According to the multi-modal data fusion-based multi-target tracking method of the legged robot, the accuracy and robustness of subsequent target detection and tracking can be improved, and continuous and stable identification and tracking of a sensitive target in a complex high-dynamic scene are realized.

The invention provides a zebra fish multi-target tracking method and system based on a space science experiment video. The method comprises the following steps: firstly, obtaining a zebra fish space science experiment video; then, motion information is extracted based on the zebra fish space science experiment video, and the motion information is used for representing a motion state represented by pixel points in the zebra fish space science experiment video; then, multi-modal feature fusion is carried out based on the motion information and the appearance information, and multi-modal fusion features are determined; and finally, target detection and tracking are carried out according to the multi-modal fusion features, and target movement track information corresponding to the target zebra fishes is determined. According to the method, motion feature information representing dynamic changes is extracted through motion modeling based on a self-adaptive threshold value, then the interaction and fusion relation between appearance and motion features is modeled step by step through a heterogeneous graph, and the robustness of target detection, data association and trajectory prediction is enhanced. Therefore, the stability and the accuracy of multi-target tracking of the zebra fish can be effectively improved.

The invention provides a laser radar odometer and static map construction method and system, and relates to the technical field of positioning and navigation.The method comprises the steps that semantic reasoning is used for obtaining point-by-point semantic tags for subsequent true dynamic point detection, and semantic constraints are provided for ICP registration; distortion removal is carried out based on motion prediction of a constant-speed motion model; performing dynamic point detection according to the shielding relation of the potential dynamic points; according to the method, multi-target tracking is carried out on a potential dynamic object to obtain state estimation, cross validation is carried out on the state estimation and a dynamic point detection result, instance-level dynamic objects based on priori pose estimation are accurately removed, unstable dynamic points are filtered out in the pre-registration stage, and the positioning accuracy and robustness of an odometer are improved. Semantic weights are introduced into ICP data association, pose estimation is obtained through robust optimization, and a static map is constructed. And high-precision and robust laser radar odometer and static map building is realized.

The invention belongs to the technical field of radar signal back-end processing, and relates to a millimeter wave radar tracking method in a multi-target complex environment, which comprises the following steps of: 1, processing a radio frequency signal received by a radar to generate millimeter wave radar point cloud data; 2, delimiting a radar action range, removing point cloud data beyond the range, and then carrying out point cloud clustering through a DBSCAN clustering method to obtain merge points; and step 3, starting a track, matching measured data with the track through data association gate judgment, if association succeeds, updating a target state and then outputting the track, and otherwise, executing track loss or maintenance operation by adopting a scoreboard mechanism. According to the method, the accuracy of target tracking can be effectively improved, the error tracking rate of multi-target tracking can be reduced, meanwhile, the calculation complexity of an algorithm is reduced, and the calculation time is greatly shortened.

The invention provides a multi-target tracking method and device based on a large model. The method provided by the invention comprises the following steps: acquiring a current frame image of a video, detecting a target in the current frame image through a detector, and generating a target detection frame and a corresponding confidence score; based on a dynamic equation of a Kalman filter, predicting the position of a track fragment in the current frame of image according to the track fragment of the previous frame of image; when the target detection frame is an effective detection frame, associating the target detection frame with the predicted trajectory fragment based on a feature coordinate matching method, and updating the position of the trajectory fragment through an observation equation after successful association; when the target detection frame is not the effective detection frame, generating a mask fragment of the target based on a mask fragment updating method, associating the mask fragment with the predicted trajectory, and updating the position of the trajectory fragment; and outputting the tracking result of the current frame, repeatedly executing the step of outputting the tracking result of each frame, integrating the tracking results of all single frames, and generating a complete tracking trajectory of all targets in the video.

The invention discloses a method for realizing holographic traffic at an intersection. The method comprises the following steps: acquiring a real-time video stream at the intersection; motor vehicles, non-motor vehicles and pedestrians in the video are identified based on a deep learning target detection algorithm, a continuous motion track of each target is generated through a multi-target tracking algorithm, identity association is carried out on cross-camera targets in combination with a re-identification algorithm, and structured traffic data including target types, real-time speeds and motion directions are output; obtaining coordinate points of each lane of the intersection based on a geographic information system, and constructing a lane network topological relation; combining with the structured traffic data to generate target trajectory data; and constructing an intersection three-dimensional model based on an unreal engine, generating a virtual target in the three-dimensional model according to the target trajectory data, and driving the virtual target to move in real time. According to the method, a holographic traffic implementation scheme is provided for a business scene which is free of radar and only monitored by a video.

The invention discloses a radar adaptive multi-target tracking method under dense clutters, which relates to the technical field of radar signal processing, adaptive filtering and multi-target tracking, and effectively reduces the calculation burden by simplifying a joint probability data association algorithm, reconstructing a confirmation matrix and directly using the confirmation matrix to calculate the association probability. Secondly, a self-adaptive extended Kalman filtering algorithm is introduced, an error covariance matrix is adjusted through a self-adaptive factor, and the tracking stability is improved; and finally, introducing a life cycle to perform target track management, dynamically determining the starting and ending of the track, and accurately tracking the high maneuvering target in the dense clutter environment. The problems of high calculation complexity, poor anti-interference performance and insufficient adaptability to a high-speed maneuvering target can be solved.

The invention provides a water column detection tracking algorithm based on a ByteTrack multi-target tracker. The algorithm comprises the following steps: step 1, obtaining video frame data of a to-be-detected object; step 2, inputting the video frame data into a static feature extraction network, performing feature extraction and feature fusion on the video frame data, and outputting a detection frame corresponding to each detected water column target and a corresponding confidence coefficient; step 3, predicting the track of the previous video frame through a Kalman filtering model to obtain a prediction track of the current frame, and outputting a prediction frame corresponding to the prediction track; and step 4, through a Hungary algorithm, based on the confidence coefficient, matching the detection frame and the prediction frame, and outputting a successfully matched water column target trajectory and a unsuccessfully matched water column target trajectory. The method can comprehensively consider the static characteristics and the dynamic characteristics of the water column, thereby providing an accurate water column detection tracking result, and providing technical support for an offshore drop point precision evaluation task.

The invention provides a dead wood quantity counting and positioning method based on an unmanned aerial vehicle ortho-video. The method comprises the following steps: constructing a data set according to an original video frame of the unmanned aerial vehicle ortho-video and synchronous positioning data; a deep learning model based on a single-stage detection architecture is trained, target detection is performed on frame-by-frame images, basic tracking is realized by detecting an associated multi-target tracker, periodic enhanced detection is performed in combination with a slice detection strategy, a redundant detection frame is eliminated through an overlapping region fusion algorithm, and a high-confidence target detection result is formed; based on a target motion track and space-time continuity analysis, appearance-tracking-loss-recovery period management of a dead wood target is realized; based on the mapping relation between the pixel coordinate system and the geographic coordinate system, the target position is converted into a GPS coordinate in combination with the flight parameters of the unmanned aerial vehicle, and a dead wood statistical result containing the space-time distribution characteristics and geographic positioning information are output.

The invention discloses a satellite video multi-target tracking method based on an optical flow and a state space model. The satellite video multi-target tracking method comprises a network model training part and a video multi-target tracking part, the network model training part comprises the steps of preprocessing a training data set, constructing and training a target detection network fused with optical flow feature enhancement, and constructing and training a multi-target sequence association network based on a state space model; the video multi-target tracking part comprises the steps of inputting an unstable image satellite video stream, detecting the position of an interested target in each frame by using a target detection network, carrying out inter-frame target association by using a multi-target sequence association network, and generating a continuous multi-target tracking trajectory. Through multi-scale optical flow enhancement, residual mask fusion and autoregression prediction, unification of high precision, real-time performance and track continuity is realized, and the method is suitable for complex and dynamic satellite video scenes.

The invention discloses a fish multi-target tracking method based on nonlinear motion modeling and category constraint, and the method comprises the following steps: constructing a basic fish target detection model which is used for generating a fish target detection frame; constructing a target tracking data set in an MOT format for target tracking and a target identification data set for appearance feature extraction, and constructing a basic fish appearance feature re-identification model for extracting appearance features of a fish target; and constructing a target tracking model associated with a nonlinear filter based on detection confidence weighting and category gating. A confidence-weighted extended Kalman filtering algorithm is provided, and the algorithm dynamically adjusts weight distribution of a detection result in a track updating process by analyzing a confidence score of a target detection frame in real time. According to the category gating mechanism, a category similarity penalty term is introduced into a cost matrix of data association, and by establishing an association barrier of category perception, it is ensured that detection frames and estimation frames of different categories are not wrongly associated to the same identity.

The invention provides a multi-target tracking information enhanced laser radar visual inertia high-precision positioning method, and belongs to the technical field of environmental perception and navigation positioning. The positioning method comprises the following steps: acquiring measurement data of a laser radar, a camera and an IMU (Inertial Measurement Unit), and performing data preprocessing, including laser radar point cloud data preprocessing, image data preprocessing and IMU pre-integration; based on the preprocessed data, establishing a target comprehensive association scale fusing a laser radar and a visual multi-modal detection bounding box and geometric features, and associating multi-modal target data; the continuous and reliable multi-target tracking and self-localization performance is realized by combining the multi-modal detection bounding box and the geometrical characteristics of the target and the IMU pre-integration measurement optimization carrier pose and dynamic target trajectory.

The invention relates to a multi-target tracking method based on dynamic semantic focus migration, and belongs to the field of intelligent vehicle target tracking. The method fuses image and language information, and comprises the following steps: firstly, respectively extracting visual features of an image and semantic features of a language instruction, obtaining a candidate target frame, constructing a semantic memory pool, and generating a semantic focus migration rule matrix describing a user attention evolution trend; secondly, guiding visual features and semantic features to perform spatial alignment based on a semantic focus migration rule matrix, and fusing visual and language information through a multi-head cross attention mechanism to obtain multi-modal fusion features; and finally, combining the fusion features and the semantic focus migration rule matrix with visual similarity to construct a multi-target association graph, optimizing a matching relationship by using a graph neural network, improving the tracking stability in complex scenes such as shielding and appearance mutation through robustness verification and strategy scheduling, and outputting a final multi-target tracking result.