TW202341072A - A high-speed data association method for multi-object tracking - Google Patents

Abstract

A high-speed data association method for multi-object tracking disclosed in the present invention is an improved high-speed data association method in a real-time multi-object tracking system. This invention uses a simple filtering operation method to delete some impossible matches based on the measured distance between the object in the previous frame and the current frame, then uses a linear weighted sum method to fuse the remaining distance information as the cost matrix of Hungarian algorithm matching, and only performs Hungarian algorithm matching twice. The high-speed data association method for multi-object tracking provided by the present invention can effectively improve the processing speed of multi-object tracking operations, especially to achieve real-time computing capabilities in embedded platforms with limited computing resources.

Description

A high-speed data association method for multi-target tracking

The present invention relates to a data association method for a tracking system, and in particular to a high-speed data association method for multi-target tracking.

Multi-Object Tracking (MOT) is one of the most challenging tasks in the field of computer vision. Among the existing multi-target tracking methods, the Tracking By Detection (TBD) algorithm has become the mainstream architecture in this field. It first uses the object detection model to detect all targets in each frame, and then Data correlation is performed on the detection results. For example, SORT and DeepSORT use the existing Faster-RCNN as the object detection model to achieve robust multi-target tracking performance.

TBD regards multi-target tracking as a problem of data association. Its purpose is to match the detection results across frames in the video sequence through data association. Among the existing methods, SORT detects the current frame through Faster-RCNN. After obtaining the category and position information of the target object, the Kalman filter is used to predict the position information of the target that was successfully tracked in the previous frame in the current frame, and then the IoU distance (Intersection over Union Distance) of the target object between the two frames is calculated. That is, calculate the overlap rate of the bounding box between the two targets, use the IoU distance of the target between the two frames as the cost matrix (Cost Matrix), and perform data association through Hungarian matching; the SORT detection model is very The above determines the tracking accuracy of the multi-target tracking system. By changing the detection model, the tracking accuracy can be increased by 18.9%.

Although SORT achieves good tracking accuracy in MOT, a large number of identity switches occur during the tracking process. The main reason is that the association metric used by SORT only occurs when the state estimation uncertainty is low. is accurate; in order to improve this problem, the DeepSORT architecture was proposed. Its SORT-based architecture adds an appearance model. In other words, DeepSORT subdivides multi-target tracking into object detection and Two steps for appearance extraction; based on these characteristics, this architecture is called Two-Step TBD; however, the Two-Step TBD architecture does not perform satisfactorily in terms of computing processing speed because both object detection and appearance extraction require a large amount of calculations, and the appearance Extraction is recalculated for the image, which results in a certain degree of repeated calculations; therefore, the architecture of One-Shot TBD has received more and more attention in recent years. Its core idea is to integrate the appearance extraction model into the object detection model to become a multi-objective Tracking models are used to complete object detection and appearance extraction at the same time, thereby sharing most of the calculations and reducing computing time.

With the continuous breakthroughs in object detection technology, the most direct benefit of the multi-target tracking model is that the tracking accuracy continues to increase, but what this brings is the increasing model size and processing time; in order to improve this problem, it is proposed The JDE architecture abandons other multi-target tracking methods and mostly uses the two-stage object detection model RCNN series for design, instead using the one-stage YOLOv3 and designing based on the One-Shot TBD architecture.

However, the above method can only reach a near-real-time computing level when running on a desktop computer using an efficient computing architecture, and cannot achieve true real-time processing performance. Specifically, according to the IPVM (IP Video Market) report, in industrial applications The average frame rate of a real-time vision system is between 11 and 20 FPS. However, existing methods cannot meet the requirement of an average frame rate between 11 and 20 FPS, and cannot be used on embedded platforms that need to run on limited computing resources.

In view of this, how to develop lightweight MOT methods for embedded systems with limited computing resources while maintaining appropriate tracking accuracy and operation speeds higher than 10FPS is still an urgent problem to be solved. Based on the One-Shot TBD architecture, the present invention proposes A lightweight real-time multi-target tracking system is developed, which is based on the lightweight network Mobilenet-SSDv2 as the object detection model, and is equipped with a high-speed data association method for multi-target tracking, using the filtering operation method of Simple Filtering. Replace the Kalman filter, based on the metric distance between the target in the previous frame and the current frame (such as IoU distance and cosine distance), after deleting some impossible matches, use the linear weighted summation method to fuse the remaining distance information As the cost matrix of Hungarian matching, and only perform the Hungarian matching twice to implement a lightweight multi-object tracking system, thereby improving the existing method's large computational load and inability to perform real-time calculations in embedded platforms with limited computing resources. application restrictions.

The high-speed data association method for multi-target tracking of the present invention can solve the difficulties that cannot be overcome by the existing technology, including: (1) Propose a low-complexity data association method, which can be combined with the existing TBD multi-target tracking technology and serve as the final data association module. (2) The proposed data association method has high-speed computing characteristics and can be operated instantly in embedded platforms with limited computing resources. Experimental results show that the processing speed can reach 12 FPS in embedded platforms. (3) The proposed data association method can still maintain the tracking accuracy and robustness of the multi-target tracking system. The experimental results show that it can achieve 58.3% of the tracking accuracy of MOTA and 48.0% of the tracking robustness of IDF1 in the MOT16 data set. (4) The proposed data association method can be applied to monitoring systems based on TBD multi-target tracking, such as self-driving visual environment perception systems, traffic monitoring systems, safety monitoring systems, etc.

The present invention provides a high-speed data association method for multi-target tracking, which includes the following steps: (a) input M detection object information in the current frame and N tracker information in the previous frame; (b) calculate the i-th The feature metric distance between the feature vector of the detected object and the feature vector of the jth tracker ;(c) Calculate the bounding box metric distance between the bounding box of the i-th detected object and the bounding box of the j-th tracker ; (d) Apply the simple filtering operation to the feature metric distance and the bounding box metric distance to obtain the filtered feature metric index respectively. and filter bounding box metrics , where i=1~M, j=1~N; (e) filter the feature metrics and filter bounding box metrics Perform a weighted sum operation to generate a cost matrix C of size M × N; (f) Apply the first linear assignment algorithm to the cost matrix C to find the best matching pair set between the current detected object and the previous tracker , unmatched detection object set, and unmatched tracker set; (g) update the corresponding tracker information based on the current detection object information in the first best matching pair set, and place the tracker in the activity In the tracking pool, clear the unmatched frame counter of the tracker so that the counter value is 0; (h) According to each previous tracker in the first unmatched tracker set, if the number of consecutive unmatched frames exceeds K frame, delete the tracker, if not, place the tracker in the inactive tracking pool, and update the tracker's unmatched frame counter; (i) Calculate the remaining M1 according to steps (a) to (e) Unmatched detection objects and all N1 trackers in the inactive tracking pool generate a cost matrix C1 of size M1 × N1; (j) Apply the second linear assignment algorithm to the cost matrix C1 to find the current unmatched detection objects. The best matching pair set between detection objects and inactive trackers, the unmatched detection object set, and the unmatched tracker set; (k) based on the current unmatched detection objects in the second best matching pair set information to update the corresponding inactive tracker information, place the tracker in the active tracking pool, and clear the unmatched frame counter of the tracker so that the counter value is 0; (l) According to the second unmatched tracking For each inactive tracker in the tracker collection, if the number of consecutive unmatched frames exceeds K frames, the inactive tracker will be deleted. If not, the inactive tracker will be placed in the inactive tracking pool and the inactive tracking will be updated. The unmatched frame counter of the device; (m) Based on each second unmatched detected object information, create a new tracker responsible for tracking the object, and place the tracker in the inactive tracking pool; (n) Output all Tracker results in the active tracking pool.

In one embodiment, in step (a), each input detected object information and tracker information includes at least one bounding box information and at least one feature vector information.

In one embodiment, in step (b), the feature metric distance is the cosine distance, and is obtained by applying the following formula (1) by using the feature vector of the detected object and the feature vector of the tracker; Formula (1): =1- In formula (1), the symbol Represents the inner product operator between two vectors.

In one embodiment, in step (c), the bounding box metric distance is the IoU distance, and is obtained by applying the following formula (2) by detecting the bounding box of the object and the bounding box of the tracker; Formula (2): =1 – IoU ( BBox _i , BBox _j ) In formula (2), the IoU function is defined as: Among them, BBox _i and BBox _j represent the bounding box of the i -th detection object in the current frame and the j -th tracking object in the previous frame; the function area(A) is to calculate the area of the input set A; the symbol Represents the intersection and union operators of two sets.

In one embodiment, in step (d), the filtering operation of simple filtering is to measure the distance of features through the following formula (3) and formula (4) after a given threshold t and bounding box metric distance Perform filtering action: Formula (3): In formula (3), SF represents the filtering operation of simple filtering; formula (4): =SF( , t ^E ), =SF( , t ^l ) In formula (4), t ^E represents the threshold of the filtering feature metric, and t ^l represents the threshold of the filtering bounding box metric.

In one embodiment, in step (e), after the weight sum operation is given as the weight value w , the filter feature metric index is calculated through the following formula (5) and filter bounding box metrics , perform weight fusion operations in a one-to-one manner to generate a two-dimensional matrix of size M × N: Formula (5): C _ij = w + (1- w ) In formula (5), C _ij represents the cost value at position (i, j) in the cost matrix, and w represents the weight parameter used to fuse the two measures.

In one embodiment, in step (f), the first linear assignment algorithm is the Hungarian algorithm.

In one embodiment, in step (g), the currently detected object information is used to update the corresponding tracker information, including storing the bounding box of the currently detected object in the tracker, and converting the feature vector of the currently detected object And the weight sum operation of the tracker's feature vector.

In one embodiment, in step (h), the K value is any positive integer greater than 0; the unmatched frame counter of the tracker is updated by adding 1 to the counter value.

In an embodiment, in step (i), M1≤M is satisfied.

In an embodiment, in step (j), the second linear assignment algorithm is the Hungarian algorithm.

In one embodiment, in step (k), the currently unmatched detected object information is used to update the corresponding inactive tracker information, including storing the bounding box of the currently unmatched detected object in the inactive tracker, and Calculate the weighted sum of the feature vectors of the currently unmatched detected objects and the feature vectors of the inactive tracker, and update the status of the inactive tracker to active.

In one embodiment, in step (l), the K value is any positive integer greater than 0.

In one embodiment, in step (m), creating a new tracker responsible for tracking the object includes storing the bounding box and feature vector of the currently unmatched detected object in the new tracker, and assigning the new tracker to the new tracker. The status is set to inactive and the unmatched frame counter value is 0.

The present invention also provides a high-speed data association method for multi-target tracking, which includes the following steps: (a) inputting M detection object information in the current frame and N tracker information in the previous frame; (b) calculating the i-th The feature metric distance between the feature vector of the detected object and the feature vector of the jth tracker ;(c) Calculate the bounding box metric distance between the bounding box of the i-th detected object and the bounding box of the j-th tracker ; (d) Apply the simple filtering operation to the feature metric distance and the bounding box metric distance to obtain the filtered feature metric index respectively. and filter bounding box metrics , where i=1~M, j=1~N; (e) filter the feature metrics and filter bounding box metrics Perform a weighted sum operation to generate a cost matrix C of size M × N; (f) Apply the first linear assignment algorithm to the cost matrix C to find the best matching pair set between the current detected object and the previous tracker , unmatched detection object set, and unmatched tracker set; (g) update the corresponding tracker information based on the current detection object information in the first best matching pair set, and place the tracker in the activity In the tracking pool, the unmatched frame counter of the tracker is cleared at the same time, so that the counter value is 0; and the motion predictor is used to predict the position of the bounding box of each tracker in the active tracking pool in the next frame, and update each The position of the bounding box of the tracker; (h) According to each previous tracker in the first unmatched tracker set, if the number of consecutive unmatched frames exceeds K frames, the tracker will be deleted; if not, the tracker will be deleted. Place it in the inactive tracking pool and update the tracker's unmatched frame counter; and use the motion predictor to predict the position of the bounding box of each tracker in the inactive tracking pool in the next frame, and update each tracking The position of the bounding box of the device; (i) According to steps (a) to step (e), calculate the remaining M1 unmatched detection objects and all N1 trackers in the inactive tracking pool, and generate a size of M1 × N1 Cost matrix C1; (j) Apply the second linear assignment algorithm to the cost matrix C1 to find the best matching pair set between the current unmatched detection objects and the inactive tracker, the unmatched detection object set, and the inactive tracker. Match tracker set; (k) Update the corresponding inactive tracker information based on the current unmatched detection object information in the second best matching pair set, place the tracker in the active tracking pool, and clear it at the same time The unmatched frame counter of the tracker, so that the counter value is 0; (l) According to each inactive tracker in the second unmatched tracker set, if the number of consecutive unmatched frames exceeds K frames, the inactive tracker will be deleted tracker, if not, place the inactive tracker in the inactive tracking pool, and update the unmatched frame counter of the inactive tracker; (m) Based on each second unmatched detected object information, create The new tracker is responsible for tracking objects and placing the tracker in the inactive tracking pool; (n) Output the results of all trackers in the active tracking pool.

In one embodiment, in steps (g) and (h), the motion predictor is a Kalman filter, an information filter, or other system state estimator.

In order to help the review committee understand the technical features, content and advantages of the present invention and the effects it can achieve, the present invention is described in detail below in the form of embodiments with the accompanying drawings and attachments, and the drawings used therein are , its purpose is only for illustration and auxiliary description, and may not represent the actual proportions and precise configurations after implementation of the present invention. Therefore, the proportions and configuration relationships of the attached drawings should not be interpreted or limited to the actual implementation of the present invention. The scope shall be stated first.

Please refer to Figure 2, which is an architecture diagram of the lightweight multi-target tracking system of the present invention. This model is based on the existing One-Shot TBD architecture and is divided into a lightweight MobileNet-JDE model and a post-processing module. The present invention uses MobileNet-SSDv2 as the proposed The basis of the MOT model, and the IR (Inverted Residual) module is used to implement a lightweight predictor (Mobile Predictor), in which the classification, regression and embedding branches are used to predict the category type, location information and appearance information of the object respectively.

The post-processing module includes non-maximum suppression (NMS) and data association processing; NMS deletes detection information with low confidence scores and high overlap rates from the detection information output by the MOT model; data association processing combines the remaining detection information with the current tracking information Matching; in the data association processing, the present invention uses a simple filtering operation method to delete some impossible matches based on the metric distance between the target in the previous frame and the current frame (such as IoU distance and cosine distance) Finally, the linear weighted summation method is used to fuse the remaining distance information as the cost matrix of Hungarian matching.

The traditional data association method is based on the Kalman filter and performs a cubic Hungarian sample matching (or linear allocation) algorithm to improve tracking accuracy (see Figure 1), but it greatly increases the computational cost; see Figure 3, This is a high-speed data association action flow chart of the present invention. In order to increase the processing speed while maintaining tracking accuracy, the present invention only needs to perform two sample matching operations, and proposes a method of using a simple filtering operation to replace the Kalman filter. device.

The following is a further explanation of the high-speed data association method of the present invention:

First, input multiple detected object information in the current frame and multiple tracker information in the previous frame.

Calculate a feature metric distance between the feature vector of the i-th detected object and the feature vector of the j-th tracker ; In one embodiment, the feature metric distance is a cosine distance, defined as the following formula (1): Formula (1): =1- In formula (1), the symbol Represents the inner product operator between two vectors.

Calculate a bounding box metric distance between the bounding box of the i-th detected object and the bounding box of the j-th tracker ; In one embodiment, the bounding box metric distance is the IoU distance defined as the following formula (2) and formula (3):

Let ( BBox _i , ) and (BBox _j, , ) respectively represent the bounding box and embedded feature vector of the i- th detection object in the current frame and the j -th tracking object in the previous frame: Formula (2): =1 – IoU ( BBox _i , BBox _j ) where the IoU function is defined as: The function area(A) is to calculate the area of the input set A; the symbol Represents the intersection and union operators of two sets.

In order to speed up the sample matching process, the simple filtering operation method of the present invention aims to filter out sample matching pairs with larger metric values as soon as possible. Therefore, the present invention applies a threshold operation to speed up the filtering process, which is defined as the following formula (3 ): Formula (3):

The threshold t is determined based on different indicators; in one embodiment, t ^E represents the threshold of the filtering feature metric, and t ^l represents the threshold of the filtering bounding box metric, so that the following formula (4 ): Formula (4): =SF( , tE) and =SF( , tl)

The above method can be used to effectively detect some bad matching pairs from a total of i × j matching pairs.

Then, based on the weighted sum of the two filtering indicators given by formula (4), an i -by- j cost matrix is created such that the following formula (5) is satisfied: Formula (5): C _ij = w + (1- w ) In formula (5), C _ij represents the cost value at position (i, j) in the cost matrix, and w represents the weight parameter used to fuse the two metrics.

Finally, the Hungarian algorithm is applied to the cost matrix to find the best set of matching pairs between the current detection and previous trackers.

It is particularly noteworthy that the present invention performs two sample matching processes using different parameter settings. The first matching is intended to determine the best matching pair set between the current detection and all trackers in the active tracking pool. The second matching Aims to determine the best set of matching pairs between currently unmatched detections and all trackers in the inactive tracking pool.

In one embodiment, the parameters of the first matching are set to ( t ^E , t ^l , w ) = (0.8, 0.5, 0.8).

In one embodiment, the parameters of the second matching are set to ( t ^E , t ^l , w ) = (0.8, 1.0, 1.0).

All remaining unmatched detections in the current frame will be used to create new trackers initialized in the inactive tracking pool; otherwise, inactive trackers that have not been active for more than 30 frames will be deleted.

Please refer to FIG. 4 , which is another high-speed data association action flow chart of the present invention. The high-speed data association method of the present invention may also include using a motion predictor, for example, using a motion predictor to predict the boundaries of each tracker in the activity tracking pool. The position of the bounding box in the next frame for each tracker in the inactive tracking pool is predicted by the motion predictor.

The type of motion predictor is not limited, and may be, for example, a Kalman filter, an information filter, or other system state estimators; in one embodiment, the motion predictor is a Kalman filter.

Please refer to Table 1 below, which shows the performance evaluation results of the MOT model (MobileNetV2) of the present invention on a desktop computer compared with the traditional multi-object tracker based on VGG-SSD, in which the information of VGG-SSD comes from Pages 21-37 of the European Conference on Computer Vision in Amsterdam, Netherlands, December 2016; from Table 3, it can be seen that when a simple filtering operation method is used to replace the Kalman filter, its The processing speed has been greatly increased to 50.5 FPS, and its tracking performance has also been significantly improved by 7.3% MOTA and 3.1 IDF1; since the simple filtering operation method does not predict the movement of the tracked target in the previous frame, the number of IDSWs in the tracking results has increased significantly. , enabling the processing speed to reach optimal levels; this advantage helps improve the real-time processing performance of MOT models running on embedded systems.

[Table 1], Backbone network model Data association MOTA (↑) IDF1 (↑) FP (↓) FN (↓) IDSW (↓) FPS* (↑) VGG-SSD Kalman filter 51.0% 44.9% 8951 77862 2564 34.3 MobileNetV2 Simple filter 58.3% 48.0% 9420 63270 3358 50.5 Performance improvement compared to VGG-SSD Replace the Kalman filter with simple filtering +7.3% +3.1% +469 -14592 +794 +16.2 *FPS: The number of frames per second processed by the entire MOT system including model inference and post-processing

Please refer to the following Table 2. Table 2 shows the computing efficiency of the present invention compared with the existing method. From Table 2, it can be seen that the data association method based on the use of simple filtering can be greatly improved without increasing the memory usage size and the number of parameters. Processing speed; and, when the backbone network model of a multi-object tracker is more robust, the simple filtering data association method will have less impact on tracking performance.

[Table 2] method Backbone network model New anchor box design Data association FLOPs Memory usage Number of parameters Model size FPS on Desktop FPS on Xavier VGG-SSD VGG-SDD No FPN No Kalman filter 183.3G 2.0 GB 36.0M 144.1 MB 34.3 5.2 JDE-1088 Dark Net53 ConV FPN No Kalman filter 271.9G 2.8 GB 73.1M 292.6 MB 1835 2.9 HarDNet512-KF HarD Net Mobile FPN Yes Kalman filter 117.2G 2.3 GB 43.9M 163.0 MB 32.7 4.0 HarDNet512-SF HarD Net Mobile FPN Yes Simple filter 40.2 4.7 MobileNet512-KF Mobile NetV2 Mobile FPN Yes Kalman filter 18.6G 2.0 GB 19.4M 78.2 MB 41.2 10.6 MobileNet512-SF Mobile NetV2 Mobile FPN Yes Simple filter 50.5 12.6

To sum up, in the present invention, a real-time lightweight MOT method based on MobileNet is proposed to effectively improve the MOT processing speed; the tracking method proposed by the present invention consists of a lightweight MOT model and a post-processing module; in the post-processing In the module, a simple filtering operation method is proposed to replace the Kalman filter used in traditional data association processing to speed up the processing. The experimental results show that the proposed MOT method is effective on desktop computers and embedded platforms respectively. When running, it can reach high-speed processing speeds of 50.5 frames per second (FPS) and 12.6 FPS; in addition, compared with the existing MOT method, the proposed method provides competitive tracking performance; these advantages make the method of the present invention suitable for Many applications run on embedded platforms, such as visual surveillance, visual tracking control of mobile robots, human-computer interaction, etc.

The above only expresses the embodiments of the present invention, but does not limit the patent scope of the present invention. For those with ordinary knowledge in the art, several modifications and improvements can be made without departing from the concept of the present invention. , these all belong to the protection scope of the present invention.

without.

Figure 1 is a flow chart of data association actions in the prior art; Figure 2 is an architecture diagram of the lightweight multi-target tracking system of the present invention; Figure 3 is a flow chart of high-speed data correlation actions according to the present invention; Figure 4 is another high-speed data correlation operation flow chart of the present invention.

Claims (16)

A high-speed data association method for multi-target tracking, including the following steps: (a) Input the information of M detected objects in the current frame and the information of N trackers in the previous frame; (b) Calculate the i-th detected object A feature metric distance between the feature vector of and the feature vector of the jth tracker ; (c) Calculate a bounding box metric distance between the bounding box of the i-th detected object and the bounding box of the j-th tracker ; (d) Apply a simple filtering operation to the feature metric distance and the bounding box metric distance to obtain a filtered feature metric index respectively. and a filtered bounding box metric , where i=1～M, j=1～N; (e) The filtering feature measurement index and the filtered bounding box metric Perform a weighted sum operation to generate a cost matrix C of size M × N; (f) Apply a first linear assignment algorithm to the cost matrix C to find the distance between the current detected object and the previous tracker A best matching pair set, an unmatched detection object set, and an unmatched tracker set; (g) update the corresponding detection object information according to the current detection object information in the first best matching pair set Tracker information, and place the tracker in the activity tracking pool, and clear the unmatched frame counter of the tracker so that the counter value is 0; (h) According to the first unmatched tracking For each previous tracker in the tracker set, if the number of consecutive unmatched frames exceeds K frames, the tracker is deleted. If not, the tracker is placed in the inactive tracking pool and the tracker is updated. Unmatched frame counter; (i) According to the steps (a) to (e), calculate the remaining M1 unmatched detection objects and all N1 trackers in the inactive tracking pool, and generate a size is a cost matrix C1 of M1 × N1; (j) apply a second linear assignment algorithm to the cost matrix C1 to find a best matching pair set between the current unmatched detection object and the inactive tracker , an unmatched detection object set, and an unmatched tracker set; (k) update the corresponding inactive tracker information according to the current unmatched detection object information in the second best matching pair set , and place the tracker in the activity tracking pool, and at the same time clear the unmatched frame counter of the tracker, so that the counter value is 0; (l) According to the second unmatched tracker For each inactive tracker in the set, if the number of consecutive unmatched frames exceeds K frames, the inactive tracker will be deleted. If not, the inactive tracker will be placed in the inactive tracking pool. And update the unmatched frame counter of the inactive tracker; (m) Based on the unmatched detected object information for each second time, create a new tracker responsible for tracking the object, and transfer the tracking Place the tracker in the inactive tracking pool; (n) Output the results of all trackers in the active tracking pool. As for the high-speed data association method for multi-target tracking described in claim 1, in the step (a), each input of the detected object information and the tracker information includes at least one bounding box information and At least one feature vector information. The high-speed data association method for multi-target tracking as described in claim 1, in step (b), the feature measurement distance is the cosine distance, and is obtained by applying the following formula (1) by using the feature vector of the detected object and the feature vector of the tracker; Formula (1): =1- In the formula (1), the symbol Represents the inner product operator between two vectors. The high-speed data association method for multi-target tracking as described in claim 1, in step (c), the bounding box measures distance is the IoU distance (Intersection over union distance), and is obtained by applying the following formula (2) by detecting the bounding box of the object and the bounding box of the tracker; Formula (2): =1 – IoU ( BBox _i , BBox _j ) In the formula (2), the IoU function is defined as: Among them, BBox _i and BBox _j represent the bounding box of the i -th detection object in the current frame and the j -th tracking object in the previous frame; the function area(A) is to calculate the area of the input set A; the symbol Represents the intersection and union operators of two sets. As for the high-speed data association method for multi-target tracking described in claim 1, in the step (d), the filtering operation of the simple filtering is to give a threshold t , through the following formula (3) and formula ( 4) Measure the distance to the feature and the bounding box metric distance Perform filtering action: Formula (3): In the formula (3), SF represents the filtering operation of simple screening; Formula (4): =SF( , t ^E ), =SF( , t ^l ) In the formula (4), t ^E represents the threshold of the filtering feature metric, and t ^l represents the threshold of the filtering bounding box metric. As for the high-speed data association method for multi-target tracking described in claim 1, in the step (e), the weight sum operation is as follows: after a weight value w is given, the weight sum calculation is performed through the following formula (5) Filter feature metrics and the filtered bounding box metric , perform weight fusion operations in a one-to-one manner to generate the two-dimensional matrix of size M × N: Formula (5): C _ij = w + (1- w ) In the formula (5), C _ij represents the cost value at position (i, j) in the cost matrix, and w represents the weight parameter used to fuse the two metrics. As for the high-speed data association method for multi-target tracking described in claim 1, in the step (f), the first linear assignment algorithm is the Hungarian algorithm. As for the high-speed data association method for multi-target tracking described in claim 1, in the step (g), the currently detected object information updates the corresponding tracker information, including storing the current detected object information. The bounding box is added to the tracker, and the weighted sum of the feature vector of the currently detected object and the feature vector of the tracker is calculated. The high-speed data association method for multi-target tracking as described in request item 1, in the step (h), the K value is any positive integer greater than 0; the unmatched frame counter of the tracker is updated. Add 1 to the counter value. The high-speed data association method for multi-target tracking as described in claim 1, in step (i), M1≤M is satisfied. As for the high-speed data association method for multi-target tracking described in claim 1, in the step (j), the second linear assignment algorithm is the Hungarian algorithm. The high-speed data association method for multi-target tracking as described in claim 1, in the step (k), the currently unmatched detected object information is used to update the corresponding inactive tracker information, including storing the current unmatched detected object information. The bounding box of the unmatched detected object is added to the inactive tracker, and the weighted sum of the feature vector of the currently unmatched detected object and the feature vector of the inactive tracker is calculated, and the unmatched detected object is added to the weighted feature vector of the inactive tracker. The status of the activity tracker is updated to active. As for the high-speed data association method for multi-target tracking described in claim 1, in the step (1), the K value is any positive integer greater than 0. The high-speed data association method for multi-target tracking as described in claim 1, in step (m), creating a new tracker responsible for tracking the object includes storing the current unmatched detected object. The bounding box and feature vector are added to the new tracker, and the state of the new tracker is set to inactive and the unmatched frame counter value is 0. A high-speed data association method for multi-target tracking, including the following steps: (a) Input the information of M detected objects in the current frame and the information of N trackers in the previous frame; (b) Calculate the i-th detected object A feature metric distance between the feature vector of and the feature vector of the jth tracker ; (c) Calculate a bounding box metric distance between the bounding box of the i-th detected object and the bounding box of the j-th tracker ; (d) Apply a simple filtering operation to the feature metric distance and the bounding box metric distance to obtain a filtered feature metric index respectively. and a filtered bounding box metric , where i=1～M, j=1～N; (e) The filtering feature measurement index and the filtered bounding box metric Perform a weighted sum operation to generate a cost matrix C of size M × N; (f) Apply a first linear assignment algorithm to the cost matrix C to find the distance between the current detected object and the previous tracker A best matching pair set, an unmatched detection object set, and an unmatched tracker set; (g) update the corresponding detection object information according to the current detection object information in the first best matching pair set Tracker information, and place the tracker in the activity tracking pool, and clear the unmatched frame counter of the tracker so that the counter value is 0; and predict the activity tracking through a motion predictor The position of the bounding box of each tracker in the pool at the next frame, and updating the position of the bounding box of each tracker; (h) based on each previous tracking in the first set of unmatched trackers If the number of consecutive unmatched frames exceeds K frames, delete the tracker. If not, place the tracker in the inactive tracking pool and update the unmatched frame counter of the tracker; and Predict the position of the bounding box of each tracker in the inactive tracking pool in the next frame via a motion predictor, and update the position of the bounding box of each tracker; (i) according to the step (i) a) to step (e), calculate the remaining M1 unmatched detection objects and all N1 trackers in the inactive tracking pool, and generate a cost matrix C1 of size M1 × N1; (j) All the The cost matrix C1 applies a second linear assignment algorithm to find a best matching pair set between the current unmatched detection objects and inactive trackers, a set of unmatched detection objects, and an unmatched tracker. Set; (k) Update the corresponding inactive tracker information according to the current unmatched detection object information in the second best matching pair set, and place the tracker in the active tracking pool , and at the same time clear the unmatched frame counter of the tracker, so that the counter value is 0; (l) According to each inactive tracker in the second unmatched tracker set, if there are consecutive unmatched If the number of frames exceeds K frames, delete the inactive tracker. If not, place the inactive tracker in the inactive tracking pool, and update the unmatched frame counter of the inactive tracker; (m) Based on each unmatched detected object information for the second time, create a new tracker responsible for tracking the object, and place the tracker in the inactive tracking pool; (n) Output Tracker results from all active tracking pools. The high-speed data association method for multi-target tracking as described in claim 15, in the step (g) and the step (h), the motion predictor is a Kalman filter, an information filter, or other systems State estimator.