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.

翻译：球员的有效跟踪与再识别是分析足球视频的关键。然而，由于球员的非线性运动、同一队伍球员外观的相似性以及频繁的遮挡，这一任务具有挑战性。因此，提取有意义的表征以表示球员对于开发有效的跟踪与再识别系统至关重要。本文提出了一种名为PRTreID的多用途基于部件的人物表示方法，该方法同时执行角色分类、队伍归属及再识别三项任务。与现有文献不同，本文通过多任务监督训练单一网络，联合解决所有三项任务。所提出的联合方法因共享主干网络而具有计算效率。此外，定量与定性结果均表明，多任务学习能够产生更丰富且更具判别性的表征。为验证PRTreID的有效性，将其与一种先进的跟踪方法集成，并采用基于部件的后处理模块处理长期跟踪。所提出的跟踪方法在具有挑战性的SoccerNet跟踪数据集上优于所有现有跟踪方法。