The SportsMOT dataset aims to solve multiple object tracking of athletes in different sports scenes such as basketball or soccer. The dataset is challenging because of the unstable camera view, athletes' complex trajectory, and complicated background. Previous MOT methods can not match enough high-quality tracks of athletes. To pursue higher performance of MOT in sports scenes, we introduce an innovative tracker named SportsTrack, we utilize tracking by detection as our detection paradigm. Then we will introduce a three-stage matching process to solve the motion blur and body overlapping in sports scenes. Meanwhile, we present another innovation point: one-to-many correspondence between detection bboxes and crowded tracks to handle the overlap of athletes' bodies during sports competitions. Compared to other trackers such as BOT-SORT and ByteTrack, We carefully restored edge-lost tracks that were ignored by other trackers. Finally, we reached the SOTA result in the SportsMOT dataset.

翻译：SportsMOT数据集旨在解决不同体育场景（如篮球或足球）中运动员的多目标跟踪问题。由于相机视角不稳定、运动员轨迹复杂以及背景繁杂，该数据集具有挑战性。以往的多目标跟踪方法无法匹配足够多的高质量运动员轨迹。为提升体育场景中多目标跟踪的性能，我们引入了一种名为SportsTrack的创新跟踪器，采用基于检测的跟踪范式。随后，我们引入三阶段匹配流程以解决体育场景中的运动模糊和身体重叠问题。同时，我们提出另一个创新点：检测边界框与密集轨迹之间的一对多对应关系，以处理体育比赛中运动员身体的重叠情况。与BOT-SORT和ByteTrack等其他跟踪器相比，我们仔细恢复了其他跟踪器忽略的边缘丢失轨迹。最终，我们在SportsMOT数据集上达到了最先进的结果。