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.

基于深度学习的物体检测和多目标跟踪进展显著，但篮球场景中的复杂多目标遮挡问题较为困难。该篇论文提出了一种名为 Basketball-SORT 的在线强鲁棒多目标跟踪方法，通过使用球员位置的投影轨迹来解决复杂多目标遮挡问题。实验证明该方法在篮球视频数据集上取得了 63.48％的高级别跟踪准确度得分，优于其他近期方法，有效地解决了复杂多目标遮挡问题。