Towards long-term player tracking with graph hierarchies and domain-specific features
This addresses robust player tracking for sports analytics, though it is incremental as it builds on existing graph-based methods with domain-specific enhancements.
The paper tackles long-term player tracking in team sports by introducing SportsSUSHI, a hierarchical graph-based method that uses domain-specific features like jersey numbers and team IDs, achieving high performance on the SoccerNet dataset and a new hockey tracking dataset.
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.