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 atthis https URL.
View on arXiv@article{stanczyk2025_2506.01373,
title={ No Train Yet Gain: Towards Generic Multi-Object Tracking in Sports and Beyond },
author={ Tomasz Stanczyk and Seongro Yoon and Francois Bremond },
journal={arXiv preprint arXiv:2506.01373},
year={ 2025 }
}