## 1 Introduction  
Multiple-object tracking (MOT), as a high-level task in computer vision, has been widely applied in various fields such as video surveillance [1; 2; 3], human behavior recognition, and autonomous driving [4; 5; 6]. One of the advantages of video-based MOT [7; 8; 9; 10; 11; 12; 13; 14] is its ability to accurately localize targets continuously in videos while maintaining their identity information, unchanged, when the appearance or the surrounding environment changes. This allows for the output of complete motion trajectories for tracked objects. As a result, it has gained increasing attention from researchers. The MOT task can be classified into two categories based on the use of video sequences: online and offline tracking. While offline tracking is capable of achieving better tracking performance by utilizing global information from videos, online tracking is more suitable for real-world applications. However, due to the fact that online tracking is unable to take advantage of subsequent image information in videos, its tracking accuracy reliesmore heavily on the detection precision of current image targets. Additionally, there has been a growing trend towards targeting specific types of objects within scenes, as many videos contain large numbers of pedestrians. Consequently, pedestrian tracking algorithms currently account for the majority of multi-object tracking methods [15; 16; 17; 18; 19; 20]. Nevertheless, there are still challenges in improving both real-time performance and tracking robustness simultaneously. As object detection algorithms continue to improve, Tracking by Detection (TBD) [21] has become a popular tracking strategy. TBD utilizes detectors to distinguish between foreground and background objects in MOT tasks, improving tracking robustness through the tracker continuous communication with the detector while predicting target positions. However, in complex scenarios such as scale transformations and frequent occlusions, target appearance features can be contaminated, leading to poor tracking results. To address this issue, researchers have utilized Re-ID deep learning models for feature extraction, followed by similarity analysis using Euclidean Distance, Cosine Distance, and other metric functions to optimize accuracy and differentiation [22]. The methods of Re-ID [23] feature extraction are different; the (Joint Detecting and Embedding, JDE) algorithm proposed by [PERSON] et al. [24] merges the pedestrian re-recognition model into the detector network, and outputs the detection information of the image and the Re-ID feature map in parallel. The detection information is then used to obtain the corresponding feature vector of the target, which avoids the redundant computation of repeated feature extraction. However, the JDE algorithm has a poor detector effect in the case of pedestrians occluding each other, and pedestrian re-recognition relies too heavily on the results of the detector. [PERSON] et al. [25] proposed FairMOT, an excellent algorithm based on the JDE integration framework. The contradiction between detection and apparent features is alleviated by fusing deep and shallow features. CenterNet [5], based on anchorless detection, is used as a detector to reduce the ambiguity of Re-ID features. The tracking accuracy is further improved and real-time requirements are met. CenterNet is a centroid-based detection algorithm with detection bias. During tracking training, classification is learned for the feature vectors of the Re-ID feature map at the center point of the target. The inability to accurately locate the effective feature positions on the feature map causes the Re-ID task to be overly dependent on the detection accuracy. Obtaining better tracking results not only improves the detection accuracy but also improves the performance of the association task. Background interference, illumination variations, pedestrian density, and target occlusion have always been difficult problems in multi-target tracking methods.