Towards long-term player tracking with graph hierarchies and domain-specific features

Maria Koshkina, James H. Elder; Proceedings of the Winter Conference on Applications of Computer Vision (WACV) Workshops, 2025, pp. 1265-1275

In team sports analytics long-term player tracking remains a challenging task due to player appearance similarity occlusion and dynamic motion patterns. Accurately re-identifying players and reconnecting tracklets after extended absences from the field of view or prolonged occlusions is crucial for robust analysis. We introduce SportsSUSHI a hierarchical graph-based approach that leverages domain-specific features including jersey numbers team IDs and field coordinates to enhance tracking accuracy. SportsSUSHI achieves high performance on the SoccerNet dataset and a newly proposed hockey tracking dataset. Our hockey dataset recorded using a stationary camera capturing the entire playing surface contains long sequences and annotations for team IDs and jersey numbers making it well-suited for evaluating long-term tracking capabilities. The inclusion of domain-specific features in our approach significantly improves association accuracy as demonstrated in our experiments. The dataset and code are available at https://github.com/mkoshkina/sports-SUSHI.