  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.
