Multi-object tracking has been a difficult problem in recent years, especially in complex scenes such as player tracking in sports videos. Player movements are often complex and abrupt. In this paper, we focus on the problem of tracking multiple players in beach volleyball videos. To handle the difficulties of player tracking, we follow the popular tracking-by-detection framework in multi-object tracking and adopt the multiple hypotheses tracking (MHT) algorithm to solve the data association problem. To improve the efficiency of the MHT, we use motion information from Kalman filter and train an online appearance model of each track hypothesis. An auxiliary particle filter method is adopted to handle the missing detection problem. Furthermore, we obtain the significant performance on our beach volleyball datasets, which demonstrate the effectiveness and efficiency of the proposed method.
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