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：体育MOT数据集旨在解决不同体育场景(如篮球或足球)中运动员的多重对象跟踪问题。 数据集具有挑战性, 因为相机视图不稳定, 运动员的复杂轨迹和复杂背景。 以往的MOT方法无法匹配运动员的高质量轨迹。 为了在体育场景中提高MOT的绩效, 我们引入了一个创新的跟踪器, 名为SportTrack, 我们用探测跟踪作为我们的探测模式。 然后我们将引入一个三阶段匹配程序, 以解决运动模糊和身体重叠的问题。 同时, 我们提出另一个创新点: 检测箱和拥挤轨迹之间的一对数通信, 以便在体育比赛中处理运动员身体重叠问题。 与其他跟踪器( 如BOT- SORT和ByteTrack) 相比, 我们仔细恢复了被其他跟踪器忽略的边线。 最后, 我们到达了SOTA结果, 以运动MOT数据集为主 。
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