## 2 Related Work  
MOT can be categorized into two common paradigms: Tracking by Detection (TBD) and Joint Detection and Tracking (JDT). TBD is divided into two stages: in the first phase, an external detector is utilized to discover and localize all the targets of interest in each image frame. The detection response is represented as a rectangular bounding box. In the second phase, data association is performed on the detection responses to achieve tracking of multiple targets. The execution time of the TBD tracking system is approximately the sum of the execution time of each of the two phases. And it becomes larger as the number of targets in the scene increases, which poses a great challenge in building a real-time, multi-target tracking system. In order to reduce computation, researchers have recently proposed the JDT paradigm to perform target detection and target tracking simultaneously. [PERSON] et al. [26] first proposed a transition paradigm from the detection-based tracking paradigm to the joint detection and tracking paradigm: the regression-based tracking paradigm, Tracktor. Tracktor transforms a target detector into a tracker by utilizing the bounding box regression of the target detector Faster RCNN [16] to directly predict the position of the target in the next frame. Compared to the detection-based tracking paradigm, Tracktor achieves better tracking performance. In MOT, Re-ID is often utilized to introduce differentiation between targets to improve the matching accuracy of the tracking algorithm and reduce the false matches. Meanwhile, Re-ID's target-specific re-recognition capability can help the trajectories of lost targets to re-match the reappearing targets and improve the tracking robustness. [PERSON] et al. [27] proposed an embedding generation module which is tightly coupled with the object detection module and a triplet-based matching module to achieve the fast Re-ID task of objects. [PERSON] et al. [28] proposed an interesting similarity-based person Re-ID framework by adopting a Siamese neural network via shared weights. Due to its powerful modeling capability, GCN is widely used in the field of pedestrian re-identification. [PERSON] et al. [29] and [PERSON] et al. [30] constructed graph models at the image level, i.e., each node of the graph represents an image in order to take into account the relationships between images; however, they ignored the relationships between different body parts of pedestrians. [PERSON] et al. [31] introduced a graph neural network to achieve contextual interaction between relevant regional features by utilizing pose-aligned connections and feature affinity connections. However, pose information extraction requires additional computation and is not integrated into the whole network for end-to-end training, which may lead to sub-optimal results. [PERSON] et al. [32] collected candidate items from the output of detection and tracking via a deep neural network and adopted a classifier with codec architecture. They also introduced Re-ID feature information for data association. In addition, the commonly used MOT data association methods [24; 25; 33; 34] only associate the detection frames with scores above a certain threshold with the trajectory, while ignoring the case when the score is below the threshold when the pedestrian is occluded, which leads to non-negligible real missed targets and fragmented trajectories. This is insufficient to depict the pedestrian's motion state and can easily cause interruption of the trajectory and ID. [PERSON] et al. [7] proposed to perform tracking by associating each detection frame instead of only the high-scoring detection frames. For low-score detection frames, we utilize their similarity to small trajectories to recover real objects and filter out background detection.