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

• URL: http://arxiv.org/abs/2502.21242v1
• Date: Fri, 28 Feb 2025 17:12:40 GMT
• Title: Towards long-term player tracking with graph hierarchies and domain-specific features
• Authors: Maria Koshkina, James H. Elder,
• Abstract summary: 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.<n>SportsSUSHI achieves high performance on the SoccerNet dataset and a newly proposed hockey tracking dataset.
• Score: 5.985204759362746
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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