Motion Fields to Predict Play Evolution in Dynamic Sport Scenes
Videos of multi-player team sports provide a challenging domain for dynamic scene analysis. Player actions and interactions are complex as they are driven by many factors, such as the short-term goals of the individual player, the overall team strategy, the rules of the sport, and the current context of the game. We show that such constrained multi-agent events can be analyzed, and even predicted, by estimating the global movements of all players in the scene at any time and used to predict play evolution. We propose a novel approach to detect the locations where the play evolution will proceed, e.g. where interesting events will occur, by tracking player positions over time. We start with a multiview video from fixed cameras of a game. We first extract the ground level sparse movement of players in each time-step, and then generate a dense motion field. Using this field we detect locations where the motion converges, implying positions towards which the play is evolving.