Effective tracking and re-identification of players is essential for analyzing soccer videos. But, it is a challenging task due to the non-linear motion of players, the similarity in appearance of players from the same team, and frequent occlusions. Therefore, the ability to extract meaningful embeddings to represent players is crucial in developing an effective tracking and re-identification system. In this paper, a multi-purpose part-based person representation method, called PRTreID, is proposed that performs three tasks of role classification, team affiliation, and re-identification, simultaneously. In contrast to available literature, a single network is trained with multi-task supervision to solve all three tasks, jointly. The proposed joint method is computationally efficient due to the shared backbone. Also, the multi-task learning leads to richer and more discriminative representations, as demonstrated by both quantitative and qualitative results. To demonstrate the effectiveness of PRTreID, it is integrated with a state-of-the-art tracking method, using a part-based post-processing module to handle long-term tracking. The proposed tracking method outperforms all existing tracking methods on the challenging SoccerNet tracking dataset.

提出了一种名为PRTreID的多功能基于部分的人物表示方法，用于追踪、重新识别并同时执行角色分类、团队关联和重新识别任务，此方法通过多任务学习得到更丰富、更有区分性的表示，并成功地应用于处理长期追踪的足球视频数据集（SoccerNet tracking dataset），在效果上超过了现有的所有追踪方法。

多任务学习用于体育视觉追踪中的联合再识别、团队归属和角色分类