## 1 Introduction  
In recent years, the multi-object tracking (MOT) algorithm has been a research hotspot in the field of computer vision, and it is widely used in intelligent transportation [1; 2], automatic driving, surveillance, smart cities [3], and motion recognition [4]. Multi-pedestrian tracking [5; 6; 7; 8; 9; 10; 11; 12; 13], which is one of the most difficult and significant parts of MOT field research, has important application value for crowd congestion, safety hazards, and pedestrian flow statistics in traffic hubs, shopping malls, and other public places. Object detection is one of the most active topics in computer vision and the basis of MOT. The continuous development of deep learning techniques has greatly improved the performance of MOT algorithms [14; 15] and has made the tracking-by-detection (TBD) two-stage pedestrian tracking algorithms [16; 17] the current mainstream framework. The TBD algorithm first detects the current frame image through the object detection network [17; 18; 19; 20; 21; 22] and obtains multiple pedestrian detection boxes, and then correlates them with the pedestrian trajectories already established in the previous sequence of video frames by the Kalman filter and Hungarian algorithm. The DeepSORT algorithm is a classical TBD algorithm that was proposed by [PERSON] et al. [23] It uses YOLOv3 [24] as the pedestrian detection network and extracts pedestrian apparent features via a pedestrian re-identification module, but because pedestrian detection and apparent feature extraction are performed in two steps, there are many redundant calculations. Track-RCNN [25]algorithm has difficulty meeting real-time requirements in pedestrian-dense scenarios, as it adds an ReID head on top of the Mask RCNN [26] and regresses a bounding box and ReID feature for each proposal. With the development of multi-task learning algorithms, one-shot pedestrian tracking algorithms with shared feature layers for pedestrian detection and apparent feature extraction have attracted the attention of scholars. [PERSON] et al. [27] proposed the first one-shot multi-pedestrian tracking algorithm, joint detecting embedding (JDE), which detects pedestrians while extracting their apparent features. Thanks to the high efficiency of one-stage object detection networks and the sharing of underlying features by multi-task joint networks, such algorithms can achieve near real-time tracking speeds. However, the YOLOv3 detection network JDEuse is anchor-based; it is very sensitive to hyperparameters and requires a lot of time for tuning, and the positive and negative samples are unbalanced during the training process. Therefore, in 2020, [PERSON] proposed the FairMOT multi-object tracking algorithm [28] based on the CenterNet [29] anchor-free detection network, which effectively makes up for the shortcomings of JDE. Because the pedestrian tracking algorithm requires extensive spatial location information, the detection network in current tracking algorithms undergoes multilayer down-sampling and convolution; a large amount of spatial location information is lost in the shallow pedestrian network, resulting in insufficient tracking accuracy and frequent IDs. Moreover, the data association method of the commonly used multi-object tracking algorithm only associates detection boxes that have scores higher than a certain threshold with the trajectories, ignoring the fact that those scores are lower than the threshold when the pedestrian is obscured, which leads to non-negligible true missing objects and fragmented trajectories. This is insufficient for depicting the pedestrian motion state and easily causes interrupted trajectories and IDs.