672 results about "Multi target tracking" patented technology

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 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 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 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 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 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 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.

The invention discloses an unmanned aerial vehicle visual angle multi-target tracking method and system based on improved ByteTrack, and belongs to the technical field of target tracking, and the method comprises the steps: training a target detection algorithm, and carrying out the target detection of a public data set through employing the trained target detection algorithm; setting a high-score confidence threshold and a low-score confidence threshold, classifying detection frames in the target detection result, and dividing the detection frames of the target detection result into high-score detection frames and low-score detection frames; an improved ByteTrack multi-target tracking algorithm is adopted, a shielding matching module is introduced, and target matching is carried out on a high-score detection frame and a low-score detection frame for three times; and updating all successfully matched track frames based on a matching result, reserving a preset frame number of unmatched track frames for subsequent frame matching, and outputting a tracking result of the current frame. The method improves the tracking accuracy and robustness, and is suitable for the multi-target tracking task in the aerial video of the unmanned aerial vehicle.

The invention provides a bridge stay cable video multi-target identification tracking and vibration extraction method based on an unmanned aerial vehicle. The method comprises the following steps: 1, constructing a bridge stay cable refined inclined slender target detection model based on a YOLOv11 model; 2, providing a multi-target tracking algorithm fusing the inclined slender target detection model and a StrongSORT algorithm; step 3, improving a displacement extraction method combining SIFT / ORB feature point matching and a sub-pixel refinement technology; 4, designing an unmanned aerial vehicle motion correction algorithm based on variational mode decomposition and time-frequency domain combined screening; and 5, constructing a joint working modal analysis algorithm for realizing combination of a natural excitation technology and a random subspace recognition algorithm. According to the method, high-precision extraction of the vibration signals of the stay cable and identification of modal parameters of the stay cable are realized, and technical support is provided for health monitoring of the large-span cable-stayed bridge.

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 mountain state real-time monitoring and identification method and system based on deep learning. The method comprises the following steps: performing multi-angle dynamic image acquisition through a PTZ camera; training a dual-target detection network based on a YOLOv7 framework, constructing a rockfall-crack detection model, and synchronously outputting bounding box coordinates of a rockfall target and position information of a crack region; constructing a rockfall trajectory prediction model through a DeepSORT multi-target tracking algorithm, and obtaining rockfall movement trajectory data; pixel-level semantic segmentation is carried out on the crack area, a crack mask graph is generated, and a crack main direction vector is extracted; carrying out registration and difference calculation on the image to obtain a crack width variation and an extension length, identifying a newly-added crack region and marking spatial distribution of the newly-added crack region; and constructing a multistage alarm mechanism which comprises multistage alarm signals of an on-site audible and visual alarm device and a cloud platform. The mountain disaster monitoring and early warning system has the advantages that efficient and real-time monitoring and early warning of mountain disasters are achieved through rockfall detection and tracking and crack analysis and prediction.

The invention discloses a referring multi-target tracking method based on cross-modal fusion and collaborative query matching, and relates to a computer vision technology. Respectively extracting visual features and language features from the video sequence and the language description of the training data set, and constructing a target query formed by splicing a detection query and a tracking query; performing cross-modal fusion on the visual features, the language features and the target query through a triple fusion module to generate multi-modal features, and inputting the multi-modal features into a decoder after residual connection and encoder optimization; the decoder is combined with a collaborative query matching mechanism to realize efficient matching of target query, a new target and a tracked target; and outputting a category, a bounding box and a reference score of the target through a prediction head module, predicting a target trajectory and calculating loss to train the model. The cross-modal feature consistency is enhanced through a triple fusion module, the detection query training efficiency is improved by means of a collaborative query matching mechanism, a target corresponding to language description is accurately tracked in a complex scene, and good adaptability and tracking precision are achieved.

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 provides a radar signal multi-target tracking method and system based on a dynamic graph convolutional network. The method comprises the following steps: acquiring a radar one-dimensional range profile sequence of a target cluster, and performing pulse compression to form a radar echo two-dimensional sequence; separating position and amplitude data of aliasing scattering points based on scattering point distribution characteristics; calculating a spatial distance between targets by using position information, constructing a dynamic edge weight in combination with a radial speed difference, and generating a shielding relation edge; aggregating neighborhood features through a dynamic graph convolutional network, and learning trajectory decoupling features of the occlusion and intersection regions; and generating association weights based on the decoupling features, inputting the association weights into a multi-hypothesis tracking algorithm, generating a multi-branch trajectory, and if the spatial distance between two branches is smaller than a dynamic threshold value and the velocity vector included angle exceeds a set angle, retaining a branch with a higher association weight, and outputting a multi-target trajectory sequence. According to the invention, the multi-target tracking precision and robustness in a shielding and trajectory crossing scene are significantly improved.

The invention relates to the technical field of target tracking, and discloses a multi-target smear-free tracking method and device in a high-dynamic scene, and the method comprises the steps: obtaining a continuous frame image sequence of a camera in the high-dynamic scene, and extracting a multi-target speed variation and a bounding box scale change sequence; performing global and local feature discrimination through a double-discriminator network to obtain a global discrimination feature vector and a local discrimination feature vector; self-adaptive scale compensation parameters are calculated, multi-source scale fusion is carried out, and a multi-scale smear removal prediction template is obtained; and carrying out light stream estimation smear elimination based on the multi-scale smear removal prediction template to obtain a first multi-target tracking result, carrying out scale consistency identity association on the first multi-target tracking result, and outputting a second multi-target tracking result. The problems of identity switching and shielding in a high dynamic scene can be effectively solved.

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 provides a real-time bird tracking and identification method and device, electronic equipment and a storage medium, and the method comprises the steps: collecting real-time bird multi-modal data which comprises video data, audio data and environment data; based on the real-time bird multi-modal data, a bird target detection model is used for detection and recognition, and a real-time bird detection and recognition result is obtained; and based on the real-time bird detection and identification result and the real-time bird multi-modal data, performing multi-target tracking by using an adaptive Kalman filtering algorithm to obtain a real-time bird tracking result. Compared with the prior art, the method of the invention has the advantages that through the bird target detection model and the adaptive Kalman filtering algorithm, the real-time bird tracking accuracy is improved; according to the method, multi-modal data, bird types and tracking information can be deeply fused and analyzed, different environmental conditions and bird types can be adapted, real-time dynamic tracking of birds can be realized, and ecological research and wild animal protection are facilitated.

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 an unmanned ship obstacle avoidance navigation system and an obstacle avoidance navigation method based on multi-sensor fusion. Comprising a ship body, a propulsion steering mechanism, a sensing layer, a time synchronization and calibration module, a fusion sensing and target tracking module, a cost map construction and dynamic obstacle prediction module, a global and local path planning module, a track tracking and control execution module and a task and safety management module. The system adopts a hardware timestamp and an ROS time base to carry out millisecond-level synchronization on multi-source data, and stable target state estimation is formed based on EKF / UKF and multi-target tracking; superposing static obstacles, dynamic prediction, historical passage popularity and a forbidding mask in the two-dimensional / three-dimensional cost graph, and performing Gaussian smoothing; improving A * of introducing course and corner cost is adopted globally, and DWA / MPC is adopted locally to carry out speed-angular speed sampling optimization; and a control side adopts a'feedforward + PID 'composite strategy to carry out closed-loop tracking on the trajectory curvature. According to the invention, reliable obstacle avoidance and intelligent navigation are realized in low-visibility and strong-reflection scenes.

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.

A method for multiple object tracking includes receiving, with a computing device, a point cloud dataset, detecting one or more objects in the point cloud dataset, each of the detected one or more objects defined by points of the point cloud dataset and a bounding box, querying one or more historical tracklets for historical tracklet states corresponding to each of the one or more detected objects, implementing a 4D encoding backbone comprising two branches: a first branch configured to compute per-point features for each of the one or more objects and the corresponding historical tracklet states, and a second branch configured to obtain 4D point features, concatenating the per-point features and the 4D point features, and predicting, with a decoder receiving the concatenated per-point features, current tracklet states for each of the one or more objects.

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 provides an intelligent traffic cone barrel anti-collision early warning method based on binocular vision, and aims to improve the safety of a road construction operation area. And accurate data are provided for subsequent detection and analysis through a binocular camera high-precision calibration technology based on circle detection. In combination with a data acquisition and enhancement technology, high-quality training data is provided for an improved RT-DETR algorithm, and the accuracy and efficiency of vehicle identification are significantly improved. A binocular vision depth perception and StrongSORT multi-target tracking algorithm is utilized to realize real-time speed measurement and distance measurement of a vehicle, and accurate input is provided for track prediction. Based on a GAN multi-modal trajectory prediction technology, a plurality of future trajectories are generated, and a multi-level early warning decision is realized through a comprehensive collision risk index (CRI). According to the method, the limitation of an existing prediction method in a complex road scene is effectively solved, the vehicle behavior intention is predicted and analyzed through the multi-modal trajectory, and reliable early warning information can be provided under various complex road conditions.

The invention discloses a cross-device multi-target tracking method and system. The method comprises the following steps: converting vehicle driving data collected by a camera and a radar into trajectory data under a global coordinate system; dividing all the sensing devices into a plurality of adjacent device pairs; checking the time dimension and the space dimension of the upstream track and the downstream track which are respectively formed by the adjacent equipment pair in sequence; and if the upstream track and the downstream track meet verification, splicing the track sequence according to a timestamp sequence to perform fusion of the global track, otherwise, temporarily storing the track as an isolated track independent of the global track. The system is used for implementing the method. The method has the advantages that feature extraction is not needed, the calculation complexity is low, and the real-time processing requirement of a large-scale sensing network can be met. Spatial similarity calculation methods are respectively designed for devices of the same type and different types, the cooperation requirements of mainstream sensing devices in an intelligent traffic scene are covered, and the compatibility is high.