Multi-task Learning for Joint Re-identification, Team Affiliation, and Role Classification for Sports Visual Tracking
This work addresses the challenge of player analysis in sports videos, which is incremental as it combines existing tasks into a single network for efficiency and improved performance.
The paper tackled the problem of tracking and re-identification in soccer videos by proposing PRTreID, a multi-task learning method that jointly performs re-identification, team affiliation, and role classification, and integrated it with a tracking method to outperform existing methods on the SoccerNet dataset.
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.