Multi-task Learning for Joint Re-identification, Team Affiliation, and
  Role Classification for Sports Visual Tracking
Amir M. Mansourian Vladimir Somers Christophe De Vleeschouwer Shohreh Kasaei
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.