In sports analysis, player tracking is essential to the extraction of statistics such as speed, distance and direction of motion. Simultaneous tracking of multiple people is still a very challenging computer vision problem to which there is no satisfactory solution. This is especially true for sports activities, for which people often wear similar uniforms, move quickly and erratically, and have close interactions with each other. In this paper, we introduce a multi-target tracking algorithm suitable for team sports activities. We extend an existing algorithm by including an automatic estimation of the occupancy of the observed field and the duration of stable periods without people entering or leaving the field. This information is included as a constraint to the existing offline tracking algorithm in order to construct more reliable trajectories. On data from two challenging sports scenarios—an indoor soccer game captured with thermal cameras and an outdoor soccer training session captured with RGB camera—we show that the tracking performance is improved on all sequences. Compared to the original offline tracking algorithm, we obtain improvements of 3–7% in accuracy. Furthermore, the method outperforms two state-of-the-art trackers.

中文翻译：

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限制团队运动的多目标跟踪

在运动分析中，运动员跟踪对于提取速度，距离和运动方向等统计信息至关重要。同时跟踪多个人仍然是一个非常具有挑战性的计算机视觉问题，目前还没有令人满意的解决方案。对于体育活动而言尤其如此，因为人们经常穿着类似的制服，快速，不规则地移动并且彼此之间有着密切的互动。在本文中，我们介绍了一种适合团队运动的多目标跟踪算法。我们扩展了现有算法，包括自动估计观测场的占用率以及稳定期的持续时间，而无需人员进入或离开该场。包含此信息是对现有脱机跟踪算法的约束，以便构造更可靠的轨迹。根据来自两个具有挑战性的运动场景的数据（用热像仪捕获的室内足球比赛和用RGB摄像机捕获的户外足球训练课程），我们显示了在所有序列上的跟踪性能都得到了改善。与原始的脱机跟踪算法相比，我们的准确性提高了3–7％。此外，该方法的性能优于两个最新的跟踪器。