A Robust Multi-Athlete Tracking Algorithm by Exploiting Discriminant Features and Long-Term Dependencies

This paper addresses multiple athletes tracking problem. Athletes tracking is the key to whether sports video analysis can be more effective and practical or not. One great challenge faced by multi-athlete tracking is that athletes, especially the athletes in the same team, share very similar appearance, thus, most existing MOT approaches are hardly applicable in this task. To address this problem, we put forward a novel triple-stream network which could capture long-term dependencies by exploiting pose information to better distinguish different athletes. The method is motivated by the fact that poses of athletes are distinct from each other in a period of time because they play different roles in the team thus could be used as a strong feature to match the correct athletes. We design our Multi-Athlete Tracking (MAT) model on top of the online tracking-by-detection paradigm whereby bounding boxes from the output of a detector are connected across video frames, and improve it from two aspects. Firstly, we propose a Pose-based Triple Stream Networks (PTSN) based on Long Short-Term Memory (LSTM) networks, which are capable of modeling and capturing more subtle differences between athletes. Secondly, based on PTSN, we propose a multi-athlete tracking algorithm that is robust to noisy detection and occlusion. We demonstrate the effectiveness of our method on a collection of volleyball videos by comparing it with recent advanced multi-object trackers.