Tracking balls in some sports--such as basketball, volleyball, and soccer--is significantly harder for machine-vision algorithms than it is for other sports.
Swiss Federal Institute of Technology in Lausanne scientist Andrii Maksai and colleagues have outlined a new means for tracking balls that improves over other approaches. Such systems assume two dissimilar strategies: in one, ball movement is followed in three dimensions (3D) to predict likely future trajectories, which are narrowed down as more data becomes available. But this method tends to fail when the ball is hidden or when players engage with the ball in unforeseen ways. The second technique involves tracking the players and observing when they have the ball, with the motion of the ball assumed to follow the player and when it is transferred between players. However, this strategy can generate imprecise tracks when lacking physics-based limits on ball movement.
"We explicitly model the interaction between the ball and the players as well as the physical constraints the ball obeys when far away from the players," says Maksai's research team.
They have assessed the algorithm on video sequences of volleyball, soccer, and basketball games recorded on multiple cameras at different angles to produce a 3D model.
From Technology Review
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