Abstract
Football player tracking is challenged by frequent occlusions, similar appearances, and rapid motion in crowded scenes. This paper presents a lightweight SAM-based tracking method combining the Segment Anything Model (SAM) with CSRT trackers and jersey color-based appearance models. We propose a team-aware tracking system that uses SAM for precise initialization and HSV histogram-based re-identification to improve occlusion recovery. Our evaluation measures three dimensions: processing speed (FPS and memory), tracking accuracy (success rate and box stability), and robustness (occlusion recovery and identity consistency). Experiments on football video sequences show that the approach achieves 7.6-7.7 FPS with stable memory usage (~1880 MB), maintaining 100 percent tracking success in light occlusions and 90 percent in crowded penalty-box scenarios with 5 or more players. Appearance-based re-identification recovers 50 percent of heavy occlusions, demonstrating the value of domain-specific cues. Analysis reveals key trade-offs: the SAM + CSRT combination provides consistent performance across crowd densities but struggles with long-term occlusions where players leave the frame, achieving only 8.66 percent re-acquisition success. These results offer practical guidelines for deploying football tracking systems under resource constraints, showing that classical tracker-based methods work well with continuous visibility but require stronger re-identification mechanisms for extended absences.
Abstract (translated)
足球运动员追踪面临频繁遮挡、相似外观和快速运动等挑战。本文提出了一种基于Segment Anything Model (SAM) 的轻量级跟踪方法，结合了CSRT跟踪器和球衣颜色外观模型。我们提出了一个团队感知的跟踪系统，利用SAM进行精确初始化，并采用HSV直方图重识别技术来改进遮挡恢复。 评估标准包括处理速度（FPS 和内存使用）、追踪准确性（成功率和框稳定性）以及鲁棒性（遮挡恢复能力和身份一致性）。实验结果显示，在足球视频序列上，所提出的方法实现了7.6-7.7 FPS的速度，并且在各种情况下的内存使用量保持稳定（约1880 MB）。该方法在轻度遮挡情况下实现了100%的跟踪成功率，在包含5名或更多球员的拥挤点球区场景中也能达到90%的成功率。基于外观特征重识别技术恢复了50%的重大遮挡情况，这表明领域特定线索的重要性。 分析揭示了一些关键的权衡：SAM+CSRT组合在不同人群密度下表现出稳定的性能，但在长时间遮挡的情况下（当球员离开画面时），重新获取成功率仅为8.66%，显示出该方法在此类情况下的局限性。这些结果为在资源受限条件下部署足球追踪系统提供了实用指导，表明经典跟踪器方法适用于持续可见的情况，但对于长期缺席的场景，则需要更强大的重识别机制。 综上所述，所提出的方法通过结合领域特定线索和轻量级模型，在处理复杂且多变的比赛环境中展现出良好的性能和实用性。
URL
https://arxiv.org/abs/2512.08467
