A two layered data association approach for ball tracking

Ball-tracking is a key technology in processing and analyzing a ball game. Because of the complexity of visual scenes, a large number of objects are usually selected as candidates for the ball, leading to incorrect identification, and conversely, the true position of the ball may sometimes be missed. In this paper, we propose a two layered data association method to improve the robustness of ball-tracking. At a local layer, we use a sliding window based Token Transfer method to generate a set of sub-trajectory candidates. At a global layer, a single ball trajectory is obtained by applying a dynamic programming based splice method to a graph consisting of the sub-trajectory candidates. We evaluated our approach on tennis matches from the Australian Open and the U.S. Open, and the results obtained show that our approach outperforms the state-of-art approach by around 30%.