Recent deep learning-based object detection approaches have led to significant progress in multi-object tracking (MOT) algorithms. The current MOT methods mainly focus on pedestrian or vehicle scenes, but basketball sports scenes are usually accompanied by three or more object occlusion problems with similar appearances and high-intensity complex motions, which we call complex multi-object occlusion (CMOO). Here, we propose an online and robust MOT approach, named Basketball-SORT, which focuses on the CMOO problems in basketball videos. To overcome the CMOO problem, instead of using the intersection-over-union-based (IoU-based) approach, we use the trajectories of neighboring frames based on the projected positions of the players. Our method designs the basketball game restriction (BGR) and reacquiring Long-Lost IDs (RLLI) based on the characteristics of basketball scenes, and we also solve the occlusion problem based on the player trajectories and appearance features. Experimental results show that our method achieves a Higher Order Tracking Accuracy (HOTA) score of 63.48$\%$ on the basketball fixed video dataset and outperforms other recent popular approaches. Overall, our approach solved the CMOO problem more effectively than recent MOT algorithms.

翻译：近年来，基于深度学习的目标检测方法推动了多目标跟踪算法的显著进步。当前的多目标跟踪方法主要关注行人或车辆场景，但篮球运动场景通常伴随着三个或更多外观相似、运动强度高且复杂的物体遮挡问题，我们称之为复杂多目标遮挡。本文提出一种在线且鲁棒的多目标跟踪方法，命名为篮球-SORT，专注于篮球视频中的复杂多目标遮挡问题。为克服该问题，我们摒弃了基于交并比的方法，转而基于球员的投影位置利用相邻帧的轨迹信息。本方法结合篮球场景特点，设计了篮球比赛约束机制与长时丢失ID重获取策略，并综合球员轨迹与外观特征解决遮挡问题。实验结果表明，在篮球固定视频数据集上，本方法的更高阶跟踪准确度得分达到63.48$\%$，优于近期其他主流方法。总体而言，本方法比现有主流多目标跟踪算法更有效地解决了复杂多目标遮挡问题。