In sports analysis, player tracking is essential to the extraction of statistics such as speed, distance and direction of\nmotion. Simultaneous tracking of multiple people is still a very challenging computer vision problem to which there is\nno satisfactory solution. This is especially true for sports activities, for which people often wear similar uniforms, move\nquickly and erratically, and have close interactions with each other. In this paper, we introduce a multi-target tracking\nalgorithm suitable for team sports activities. We extend an existing algorithm by including an automatic estimation of\nthe occupancy of the observed field and the duration of stable periods without people entering or leaving the field.\nThis information is included as a constraint to the existing offline tracking algorithm in order to construct more reliable\ntrajectories. On data from two challenging sports scenariosââ?¬â?an indoor soccer game captured with thermal cameras\nand an outdoor soccer training session captured with RGB cameraââ?¬â?we show that the tracking performance is\nimproved on all sequences. Compared to the original offline tracking algorithm, we obtain improvements of 3ââ?¬â??7% in\naccuracy. Furthermore, the method outperforms two state-of-the-art trackers.
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