无需训练，用预训练模型实现通用多目标追踪
No Train Yet Gain: Towards Generic Multi-Object Tracking in Sports and Beyond
- 利用时序传播的分割掩码作为关联线索提升鲁棒性
- 在多个体育和行人数据集上表现优异，无需逐视频调参
- 适合希望快速部署且不依赖训练的追踪应用场景
多目标追踪（MOT）对体育分析至关重要，可提供性能评估与战术洞察。但体育场景中存在高速运动、遮挡和摄像机位移等挑战。传统检测后追踪方法需大量调参，基于分割的方法则在轨迹处理上存在困难。我们提出 McByte，一种无需训练的检测后追踪框架，通过引入时序传播的分割掩码作为关联线索，显著提升鲁棒性。该方法仅依赖社区常用的预训练模型与目标检测器，不需额外训练。在 SportsMOT、DanceTrack、SoccerNet-tracking 2022 与 MOT17 上的实验表明，McByte 在体育与一般行人追踪任务中均表现强劲。结果验证了掩码时序传播在提升追踪泛化能力方面的有效性。代码将开源于 https://github.com/tstanczyk95/McByte。
原文摘要 · Abstract (English)
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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