No Train Yet Gain: Towards Generic Multi-Object Tracking in Sports and Beyond

• URL: http://arxiv.org/abs/2506.01373v1
• Date: Mon, 02 Jun 2025 07:00:15 GMT
• Title: No Train Yet Gain: Towards Generic Multi-Object Tracking in Sports and Beyond
• Authors: Tomasz Stanczyk, Seongro Yoon, Francois Bremond,
• Abstract summary: Multi-object tracking (MOT) is essential for sports analytics, enabling performance evaluation and tactical insights.<n>Traditional tracking-by-detection methods require extensive tuning, while segmentation-based approaches struggle with track processing.<n>We propose McByte, a tracking-by-detection framework that integrates temporally propagated segmentation mask as an association cue to improve robustness without per-video tuning.
• Score: 1.0806835533814036
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-object tracking (MOT) is essential for sports analytics, enabling performance evaluation and tactical insights. However, tracking in sports is challenging due to fast movements, occlusions, and camera shifts. Traditional tracking-by-detection methods require extensive tuning, while segmentation-based approaches struggle with track processing. We propose McByte, a tracking-by-detection framework that integrates temporally propagated segmentation mask as an association cue to improve robustness without per-video tuning. Unlike many existing methods, McByte does not require training, relying solely on pre-trained models and object detectors commonly used in the community. Evaluated on SportsMOT, DanceTrack, SoccerNet-tracking 2022 and MOT17, McByte demonstrates strong performance across sports and general pedestrian tracking. Our results highlight the benefits of mask propagation for a more adaptable and generalizable MOT approach. Code will be made available at https://github.com/tstanczyk95/McByte.

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