Tennis Player Segmentation for Semantic Behavior Analysis

Tennis player silhouette extraction is a preliminary step fundamental for any behavior analysis processing. Automatic systems for the evaluation of player tactics, in terms of position in the court, postures during the game and types of strokes, are highly desired for coaches and training purposes. These systems require accurate segmentation of players in order to apply posture analysis and high level semantic analysis. Background subtraction algorithms have been largely used in sportive context when fixed cameras are used. In this paper an innovative background subtraction algorithm is presented, which has been adapted to the tennis context and allows high precision in player segmentation both for the completeness of the extracted silhouettes. The algorithm is able to achieve interactive frame rates with up to 30 frames per second, and is suitable for smart cameras embedding. Real experiments demonstrate that the proposed approach is suitable in tennis contexts.