Content-Based Model Template Adaptation and Real-Time System for Behavior Interpretation in Sports Video

In this paper, we present a real-time sports analysis system, which not only recognizes the semantic events, but also concludes the behavior, like player’s tactics. To this end, we propose an advanced multiple-player tracking algorithm, which addresses two improvements on practical problems: (1) updating of the player template so that it remains a good model over time, and (2) adaptive scaling of the template size depending on the player motion. In this algorithm, we obtain the initial locations of players in the first frame. The tracking is performed by considering both the kinematic constraints of the player and the color distribution of appearance, thereby achieving promising results. We demonstrate the performance of the proposed system by evaluating it for double tennis matches where the player count and the resulting occlusions are challenging.