论文标题
Sportstrack：一种用于跟踪运动场景运动员的创新方法
SportsTrack: An Innovative Method for Tracking Athletes in Sports Scenes
论文作者
论文摘要
SportsMot数据集旨在解决在篮球或足球等不同运动场景中运动员的多个对象跟踪。由于相机视图，运动员的复杂轨迹和复杂的背景，该数据集具有挑战性。以前的MOT方法无法匹配足够的运动员高质量轨道。为了在体育场景中追求更高的MOT性能，我们介绍了一个名为Sportstrack的创新跟踪器，我们将追踪通过检测作为检测范例。然后，我们将引入一个三阶段的匹配过程，以解决运动场景中的运动模糊和身体重叠。同时，我们提出了另一个创新点：检测Bboxes和拥挤的轨道之间的一对一对应，以应对运动比赛中运动员的身体的重叠。与其他跟踪器（例如Bot-Sort和Bytetrack）相比，我们仔细恢复了其他跟踪器忽略的边缘轨道。最后，我们在SportsMot数据集中达到了SOTA结果。
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.
