385 results about "Multi target tracking" patented technology

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 method, a system and equipment for intelligently identifying unsafe behaviors of coal mine operating personnel and a medium. The method comprises the following steps: extracting individual trajectory data from an underground video stream through a multi-target tracking algorithm, decomposing continuous actions into an atomic behavior sequence with a space-time mark by using attitude estimation and a space-time diagram convolutional network, and capturing space-time relevance of a behavior chain through an attention mechanism enhanced long and short term memory network to generate a feature vector; risk reasoning is carried out in combination with the coal mine safety knowledge graph to predict the risk event type and probability, and finally early warning information is generated based on a multi-level early warning strategy. According to the scheme, the crossing from isolated action recognition to behavior chain risk prediction is realized, and the early warning capability of potential safety risks in coal mine operation is remarkably improved through fusion of space-time correlation analysis and domain knowledge of the behavior sequence.

The invention discloses a method for monitoring and identifying dangerous behaviors of a smart park, and relates to the field of intelligent security and protection. The method comprises the following steps: acquiring an environment data stream through a sensing device deployed at a key point location of a park and carrying out standardization processing on the environment data stream; the data stream is sent to an edge computing node, a target is recognized through a lightweight target detection model, the detection confidence coefficient is obtained, a motion track is generated through a multi-target tracking algorithm, the rule matching degree is calculated according to a predefined rule base, and dangerous behaviors are preliminarily recognized; calculating a comprehensive model prediction credibility based on the detection credibility and the rule matching degree, and generating a final comprehensive credibility through a multi-dimensional credibility fusion model in combination with the time sequence consistency credibility and the scene matching credibility; and dangerous behavior trend prediction is carried out according to the comprehensive confidence, and an alarm instruction is generated to trigger disposal linkage, so that advanced prevention and quick response of dangerous behaviors are realized, and the intelligent level and operation and maintenance efficiency of park security and protection are greatly improved.

The invention discloses a real-time multi-target tracking method based on occlusion information. The real-time multi-target tracking method specifically comprises the following steps: step 1, initializing a tracker; step 2, obtaining a current frame target and appearance characteristics of the current frame target, and sending the obtained target and corresponding characteristics into a tracker; 3, constructing an incidence matrix, and carrying out two-stage global matching to obtain a matching result M; step 4, correcting the influenced target based on the division of the track category in the step 1 and the division of the target category in the step 3; then the track information is updated; 5, increasing the shielding duration of the unmatched tracks, deleting the tracks with overlong shielding duration, converting the types of the tracks, and initializing the unmatched targets; and step 6, after track category conversion is completed, outputting a current frame tracking result, and returning to the step 2 for cyclic tracking until tracking is finished. According to the method, the tracking effect on the low-confidence target in the low-camera-angle scene is effectively improved, and the accuracy of the multi-target tracking task is improved.

The invention provides a target positioning method and system based on a distributed networking radar system, and belongs to the technical field of radars, and the method comprises the steps: controlling a plurality of radar nodes to transmit stepping linear frequency modulation continuous wave pulse signals, and collecting target echoes; carrying out band-pass filtering, pulse compression and fast time synchronization processing on the echoes, and generating interference-free data through a moving target extraction algorithm; realizing cross-node clock synchronization based on a moving target behavior cognition model and a template matching algorithm; constructing a multi-node distance estimation equation set, generating an accurate distance value through calibration broadband synthesis and FFT, and determining a target three-dimensional coordinate; and generating a motion track and speed prediction through Kalman filtering based on the target position sequence. The system comprises a signal emission and acquisition module, a signal processing module, a clock synchronization module, a distance estimation and coordinate fusion module and a trajectory prediction module. The method supports multi-target tracking, improves positioning precision and time synchronization precision, has few errors, and is compatible with any node deployment form.

The invention discloses a target tracking model based on an enhanced target detection algorithm, and relates to the technical field of real-time target tracking. A CD-YOLO detector is constructed; a robust multi-target tracker is constructed; constructing a collaborative detection-tracking optimization mechanism; evaluating and optimizing the performance; according to the method, a deformable convolution and coordinate attention mechanism is introduced into YOLOv10, so that the perceptual ability of the model to geometric deformation and spatial information is enhanced; the CD-YOLO and the enhanced StrongSORT tracker are combined, so that the detection precision and the tracking stability of the system in complex scenes such as dense crowds, serious shielding and variable visual angles are remarkably improved; a collaborative optimization mechanism between detection and tracking is established, so that a detection result and tracking feedback can be mutually enhanced, a closed-loop robust tracking process is formed, and the overall precision and stability are remarkably improved on the premise that the real-time performance of the system is not affected.

The invention discloses a multi-target tracking method for an offshore rain, fog and jitter environment, and the method comprises the steps: obtaining multi-target image data in the offshore rain, fog and jitter environment, and carrying out the target cutting and fusion of the multi-target image data based on a target enhancement segmentation strategy, and obtaining an enhanced sample image; mapping the enhanced sample image and target features in the multi-target image data into a candidate frame set of a pixel scale, and obtaining a sample data set containing adaptive Anchors according to the candidate frame set based on a clustering algorithm; performing model training on the multi-target detection network through the sample data set to obtain an optimal detection model so as to realize target detection; and defining a target state vector and a target observation vector according to a detection result, and realizing multi-target tracking under the marine rain and fog and jitter environment based on an improved Kalman filtering algorithm. The problems that in the prior art, the precision of multi-target detection under the marine rain and fog and jittering environment is insufficient, and a systematic solution for multi-target tracking under the marine rain and fog environment and the jittering scene is lacked are solved.

The present application discloses an image capture method for an image capture system, a handheld gimbal, and an unmanned aerial vehicle. The method comprises: acquiring a tracking activation instruction for instructing activation of multi-target tracking; on the basis of the tracking activation instruction, identifying a plurality of target objects in an image captured by an image capture device, and tracking the plurality of target objects in the captured image; and on the basis of tracking information of the plurality of target objects, automatically adjusting the orientation of the image capture device and / or image capture parameters of the image capture device, such that the plurality of target objects remain within the captured image. Tracking and image capture can be activated on the basis of an acquired tracking activation instruction, and in group tracking scenarios such as group dancing, group photos, or stage performances, the orientation and / or image capture parameters of an image capture device can be automatically adjusted on the basis of tracking information of a plurality of target objects in an image captured by the image capture device, such that the plurality of target objects remain within the captured image, thereby effectively improving multi-target tracking and image capture performance in group tracking scenarios.

The invention discloses a high-rotating-speed hydraulic generator rotor bolt looseness detection method and system, belongs to the technical field of hydropower station high-rotating-speed rotating mechanical equipment detection, and aims at solving the technical problems that hydraulic generator rotor bolt looseness detection is low in efficiency and poor in precision, real-time monitoring cannot be achieved, and safe and stable operation of a hydropower station is seriously affected. According to the invention, the image of the rotor bolt area is collected, and the image is accurately processed by using an image correction algorithm, so that image distortion is eliminated; thirdly, fusing an improved YOLOv8 target detection technology, a DeepLabV3 + semantic segmentation technology and a DeepSort multi-target tracking technology, and accurately extracting angle features of the bolt marking line; and then, a looseness identification objective function is constructed based on the angle change of the bolt, the function is solved in real time, and a detection result is output. According to the invention, high-precision, non-contact and real-time bolt looseness detection is realized, the detection efficiency and reliability are effectively improved, and a solid guarantee is provided for safe operation of a hydropower station.

The invention discloses a millimeter-wave radar fine-grained human body pose sensing method based on multi-dimensional feature extraction, and belongs to the field of computer vision and radar sensing, and the method comprises the steps: managing a millimeter-wave radar sensor through an equipment access module to generate a 4D point cloud; the target tracking module performs multi-target tracking on the 4D point cloud, predicts a target position by using a Kalman filter, divides point cloud affiliation through a gate function, creates a new target by using DBSCAN clustering, eliminates continuous targets without point cloud affiliation, and obtains a target center position; the human body pose detection module converts point clouds to a local coordinate system for normalization based on a target center position and 4D point clouds, fuses continuous multi-frame point clouds, and inputs a deep learning network of PointNet and U-Net improved based on an attention mechanism to output human body pose joint point coordinates. According to the invention, the human body pose detection precision in a limited data mode is improved, and a human body health monitoring function available to a home environment is realized.

The invention belongs to the technical field of distributed multi-sensor passive detection multi-target tracking. The invention provides a factor graph optimization passive detection multi-target tracking method based on a Gaussian mixture model. According to the embodiment of the invention, the multi-target batch number is distributed by constructing the distributed passive sensor cooperative coordinate system and combining the multi-target identity judgment result between the two sensors; calculating direction finding lines based on two-dimensional observation of a sensor, combining the direction finding lines of the same batch number, obtaining a multi-target position estimation point set through a least square method, and obtaining a multi-target coarse positioning point through weighted fusion; modeling by adopting a Gaussian mixture model, fusing measurement distribution characteristics, solving parameters through an expectation maximization algorithm, and completing solvable conversion of an optimization problem; a factor graph optimization model containing multiple factors is constructed, state estimation is achieved through sliding window optimization, and track association and state updating are completed in combination with the Mahalanobis distance and the Hungary algorithm; and the passive detection multi-target tracking performance of the distributed sensor is effectively improved.

The invention discloses a phased array radar signal processing system with deterministic delay, and relates to the technical field of semiconductors, the system carries out equal-length wiring design on a link in a hardware level, a timestamp marking circuit is embedded in the link, a core processing module carries out multi-channel data alignment based on timestamp marks in a software level, and the data alignment is carried out based on the timestamp marks. The multi-channel data transmission delay deviation is solved through elastic buffer control, the most reasonable buffer depth and release phase parameters are determined through calculation, and therefore the buffer delay is minimized, the system achieves deterministic minimum delay through software and hardware collaborative design, data alignment and quick response are guaranteed, and the system is suitable for large-scale popularization and application. Low-delay and high-consistency transmission and processing of multi-channel signals are achieved, so that high-precision pointing control of radar beams and time consistency of multi-target tracking are achieved, and the high-synchronization and low-jitter requirements of phased array radar for high-speed signal processing are met.

The invention discloses a vehicle target motion state estimation method based on an unmanned aerial vehicle video, and relates to the technical field of computer vision and intelligent monitoring, and the method comprises the steps: receiving a real-time video stream of an unmanned aerial vehicle, decoding the real-time video stream to obtain an original video frame, carrying out the graying, zooming and normalization preprocessing to obtain a processed image, and synchronously adjusting the size of a vehicle bounding box; vehicle targets are detected, a unique ID is allocated for multi-target tracking, and a motion trail is maintained; determining a background feature region, detecting and tracking feature points to estimate an inter-frame transformation matrix, and performing accumulative transformation to obtain a background overall transformation relation and a historical frame-to-current frame mapping mechanism; calculating a displacement residual error based on the trajectory and the mapping, judging direction consistency, estimating a smooth speed, and updating a motion confidence coefficient to judge a vehicle motion or static state and static duration; outputting the ID, the state, the static duration and the average speed of the vehicle; the method effectively eliminates the motion interference of the unmanned aerial vehicle, improves the estimation precision, is suitable for the traffic monitoring of the unmanned aerial vehicle, and is high in real-time performance and reliability.

The application provides a multi-target tracking method, device and vehicle, and relates to the field of multi-target tracking.The method comprises the following steps: acquiring laser radar data and millimeter wave radar data collected by a laser radar and a millimeter wave radar at a current time, and determining detection target information at the current time according to the target data; matching and tracking a plurality of obstacles in a preset range based on the detection target information and historical tracking target information; and the historical tracking target information comprises position information, speed information and a heading angle of each obstacle that needs to be tracked in a tracking list.The multi-target tracking method, device and vehicle provided by the application are used for enabling an unmanned working machine to have a stable tracking function for multiple targets, and improving the working efficiency of the working machine.

The invention relates to a fish body detection counting method for a channel behind a fish pump of a fishing boat, and provides an NMS-free fish body detection model MGI-RTDETR based on an improved RT-DETR for small targets, rapid movement, strong blur, highlight shielding and limited shipborne computing power. The model integrates multi-scale grouping interaction, dynamic context mixing, detail fidelity fusion, deployment period re-parameterization and back projection up-sampling, and real-time detection of one fish and one frame is achieved. And in combination with lightweight multi-target tracking, outputting paragraph counting by adopting a one-way cross-line combined de-duplication strategy. According to the method, missing detection and time delay can be remarkably reduced, edge deployment is facilitated, and the method is suitable for near-real-time fishing estimation, operation monitoring and resource evaluation.

The invention belongs to the technical field of computer vision and intelligent video analysis, and particularly relates to a multi-target tracking method based on trajectory recovery. The method comprises the following steps: firstly, performing target detection on an input video frame to obtain a bounding box, confidence and related feature information of a candidate target; then, a current detection result is matched with a historical track through Kalman filtering and appearance features, and track updating of the first stage is achieved; when an unmatched target exists, the position of the unmatched detection frame is dynamically corrected by calculating the average displacement of the center points of the front and back frame detection target, and whether the distance between the target and the camera is lower than the preset track recovery threshold value is judged by combining the distance information between the target and the camera. And when the conditions are met, the target is directly activated and recovered to be an effective tracking trajectory of the current frame, so that quick re-association of the lost target is realized.

The invention provides a radar multi-target tracking and imaging echo simulation method and system, and belongs to the technical field of radar echo simulation. The method comprises the following steps of: pushing the position and the motion state of a radar platform and RCS point set data of a plurality of targets to an impact function generation FPGA (Field Programmable Gate Array), and latching data through the FPGA to form a multi-target data set when a radar signal is detected; point set data, postures and world coordinates of the targets in the multi-target data set are split into a plurality of calculation instances, sub-range profile data of each calculation instance are calculated in parallel, the sub-range profile data of the multiple calculation instances are merged and superposed into range profile data of a single target, similarly, range profile data of all the targets are calculated in parallel, and the range profile data of all the targets are calculated in parallel; and after obtaining multi-target range profile data, carrying out convolution on the multi-target range profile data and a radar signal, after obtaining a convolution signal, carrying out signal delay and multi-branch amplitude-phase modulation, and outputting the amplitude-phase modulated convolution signal. According to the invention, signal echo simulation can be carried out in parallel pipeline, and high-quality large-scene echo signals are output.

The invention relates to a multi-target tracking method and device and a storage medium. According to one embodiment, the method includes: determining a plurality of target whole body detection frames in a current input image by performing target whole body detection, and determining a plurality of target head detection frames in the current input image by performing target head detection; determining whole body identifiers of the plurality of target whole body detection frames by executing whole body trajectory association; determining head identifiers of the plurality of target head detection frames by executing head trajectory association; determining a plurality of target whole body prediction frames corresponding to the plurality of target head detection frames based on the positions and sizes of the plurality of target head detection frames; determining a target whole body association frame of the plurality of target head detection frames in the plurality of target whole body detection frames based on areas occupied by the plurality of target whole body prediction frames of the plurality of target head detection frames in the input image; and updating the target whole body trajectory set based on the whole body identifier of the target whole body association frame.

The invention discloses a multi-target tracking method and system for unmanned aerial vehicle small targets, and belongs to the technical field of computer vision. The method comprises the steps of obtaining multi-level feature maps based on a video frame image, and then unifying the multi-level feature maps into an initial fusion feature map; calculating to obtain a two-dimensional enhanced feature map based on the initial fusion feature map and the original image; calculating to obtain a spatial enhanced feature map based on the two-dimensional enhanced feature maps of the front frame and the rear frame; after splicing the two-dimensional enhanced feature maps of the front frame and the rear frame, sequentially performing feature alignment processing and memory pool interaction processing to obtain a time enhanced feature map of the current frame, and updating the memory pool; and fusing the spatial enhanced feature map and the time enhanced feature map, performing down-sampling and splicing, and further combining an output result with a ByteTrack tracking strategy to realize multi-target tracking. According to the method, the accuracy and identity consistency are remarkably improved, and meanwhile, efficient and real-time reasoning performance is kept.

The present application relates to the technical field of robot control, and especially relates to a multi-robot optical processing safety control system and method based on machine vision. The method covers the global vision of multi-robot collaborative processing through multiple vision sensors, and tracks multiple feature targets in the workspace in parallel. The high-reflective marker points on the robots in the image are located at the sub-pixel level. After the pixel boundary box is used to determine and issue a primary early warning event or a collision warning event, the pose solution frequency of the involved robot is dynamically adjusted, and the three-dimensional space pose data of the robot is solved. Whether to execute motion intervention or emergency stop is determined according to the three-dimensional space shortest Euclidean distance and the predicted collision time between the involved robots. The present application uses a low-complexity two-dimensional multi-target tracking method for global continuous monitoring, and performs high-precision pose solution and collision detection in stages, so as to realize dynamic optimization allocation of computing resources, avoid monitoring false negatives, and effectively reduce system running load.

The application discloses a kind of based on multi-objective optimization algorithm's advertisement putting real-time effect tracking method, advertisement putting data analysis technical field, including step one: obtaining advertisement putting real-time behavior data;Step two: constructing advertisement putting effect window matrix;Step three: through improved MiniRocket network, execute advertisement effect conduction difference feature extraction and continuous response fragment aggregation processing;Step four: constructing multi-objective tracking objective function set and multi-objective optimization constraint condition;Step five: using improved MOEA / D algorithm, execute state traction decomposition optimization and fragment duration constraint neighborhood replacement processing;Step six: by Canberra distance, execute target deviation analysis processing;Step seven: match target advertisement plan's real-time effect tracking state.The application improves the accuracy of advertisement putting real-time effect tracking by improved MiniRocket network and improved MOEA / D algorithm.

The invention discloses an AI vision intelligent inspection and foreign matter recognition emergency shutdown system for a belt conveyor. The system comprises data acquisition and sample construction; constructing a DETR foreign matter recognition model; images are collected in real time, and online recognition is conducted through a DETR foreign matter recognition model; carrying out target tracking and behavior analysis based on a multi-target tracking algorithm of ByteTrack; a distance threshold value, a speed threshold value and a score threshold value are set; graded alarming is carried out, and a shutdown signal is sent to the PLC control unit under the shutdown condition. The system comprises an alarm module, an edge calculation unit, a communication module and a PLC control unit. The AI inspection camera, the edge calculation unit, the communication module and the PLC control unit are connected in sequence; the edge calculation unit comprises an AI identification module and a tracking and behavior analysis module. According to the invention, real-time identification, behavior judgment and automatic safe shutdown linkage control of foreign matters or abnormal states on the conveying line can be realized.

The invention provides a multi-target trajectory tracking method and device, electronic equipment and a storage medium, and relates to the technical field of computers. The method comprises the following steps: determining a forward time sequence track and a reverse time sequence track; calculating a fusion index between every two tracks in a track set, wherein the track set comprises each forward time sequence track and each reverse time sequence track; performing trajectory fusion on the two trajectories, and adding the trajectory obtained by fusion into a trajectory set; and iteratively calculating a fusion index of every two trajectories in the current trajectory set until the fusion indexes do not meet a preset fusion condition, and taking the current trajectory set as a first target set. According to the invention, the forward time sequence track and the reverse time sequence track are obtained, the time sequence information is fully utilized, and the accuracy and robustness of multi-target track tracking are improved. The accuracy of the multi-target tracking algorithm is further improved through trajectory fusion. And two tracks belonging to different targets can be further prevented from being mistakenly associated by determining the fusion index.

The invention relates to a real-time error model enhanced vehicle-road collaborative integrated navigation method and system, point cloud features of a vehicle and a position error of a detection algorithm are extracted through a real-time error model, sample error distribution in each discrete grid is counted through discrete point cloud feature space to obtain a sample error covariance, and the covariance of the sample error is calculated to obtain the real-time error model enhanced vehicle-road collaborative integrated navigation. And training and predicting a sample error covariance by using a random forest model. The system comprises a point cloud target detection module, an image target detection module, a multi-target tracking module, a real-time error model module, a communication module and a vehicle-mounted terminal integrated navigation module, and in vehicle-road collaborative integrated navigation, error covariance predicted by a real-time error model is used for replacing error covariance obtained through statistics, so that the accuracy of vehicle-road collaborative integrated navigation is improved. After the filter is input, the positioning precision is effectively improved.

The invention discloses an automatic focusing method and device, computer equipment and a computer readable storage medium, and relates to the technical field of focusing. The method comprises the following steps: performing multi-target tracking detection on a preview image frame to obtain a face frame set, a human eye frame set and a multi-target tracking parameter; performing scale measurement aperture unification on the face frame set and the eye frame set to obtain a target face frame width and a target eye frame width; calculating a human face available threshold value and a human eye available threshold value; determining a depth-of-field coverage judgment result of the focusing target according to a depth map corresponding to the preview image frame; and according to the target face frame width, the target eye frame width, the multi-target tracking parameter after smoothing normalization, the depth-of-field coverage judgment result, the face available threshold and the eye available threshold, determining a focusing strategy to perform focusing switching. Therefore, the stability of the exported picture, the accurate focus and the smooth switching are ensured through automatic focusing.

A multi-target tracking and monitoring method and system with high trajectory consistency, belonging to the field of computer vision, solves the problems of identity switching and trajectory fragmentation caused by long-term target occlusion and dense interaction in multi-target tracking, especially in UAV monitoring scenarios. The method includes the following steps: 1. Inputting a video sequence, performing target detection and feature acquisition for each frame; 2. Performing preliminary association through a basic tracker to obtain an initial trajectory; 3. Detecting the disappearance and appearance of abnormal trajectories using a position awareness submodule; 4. Storing abnormal trajectory features using a long-term memory submodule; 5. Performing secondary association using a multi-step cross-frame matching submodule; 6. Completing trajectory updates. This invention is applicable to UAV monitoring scenarios such as high-altitude inspection, low-altitude operations, and outdoor scene monitoring.

The application provides a GM-AI-PHD multi-target tracking method based on kernel density estimation, the filter uses a kernel density estimation method to estimate an amplitude probability density function of a target and clutter in real time, solves the problem that a GM-AI-PHD multi-target tracking method based on parameter estimation can only be applied to specific scenes, and can realize real-time and accurate positioning and tracking of multi-targets in a complex environment (a large number of clutters, unknown statistical distribution of a real likelihood function).

The present invention provides a method, apparatus, and storage medium for multi-target tracking. [Solution] The method involves performing target whole-body detection to determine multiple target whole-body detection frames and determining multiple target head detection frames in the current input image, performing whole-body trajectory association to determine whole-body identifiers for the multiple target whole-body detection frames, performing head trajectory association to determine head identifiers for the multiple target head detection frames, determining multiple target whole-body prediction frames corresponding to the multiple target head detection frames based on the position and size of the multiple target head detection frames, determining target whole-body related frames among the multiple target whole-body detection frames based on the area of ​​the multiple target whole-body prediction frames corresponding to the multiple target head detection frames in the input image, and updating the target whole-body trajectory set based on the whole-body identifiers of the target whole-body related frames of the multiple target head detection frames.

The invention discloses a diffusion model multi-target tracking method based on learnable motion condition representation, and belongs to the technical field of computer vision and intelligent video analysis. The method comprises the steps of firstly obtaining adjacent frame images of a video sequence and performing feature extraction; then constructing a motion prior by using a track in a historical target frame of a previous frame and a predicted target frame of a current frame, and compressing the motion prior into a motion condition representation; aligning the motion condition representation with the candidate target frame of the current frame through intersection-to-union ratio matching to obtain a corresponding motion condition feature; in the diffusion type iteration updating network, enabling a motion condition to participate in diffusion denoising prediction and a back diffusion updating process, and outputting an association score between adjacent frame candidate target frames; and finally, data association and trajectory state updating are completed according to the association score, and multi-target tracking result output is realized. According to the method, the accuracy of target position updating and the stability of track association are improved, and the multi-target tracking capability in a complex motion scene is enhanced.