Automated Stroke Classification in Tennis

摘要

Stroke recognition in tennis is important for building up statistics of the player and also quickly analyzing the player. It is difficult primarily on account of low resolution, variability in strokes of the same player as well as among players, variations in background, weather and illumination conditions. This paper proposes a technique to automatically classify tennis strokes efficiently under these varying circumstances. We use the geometrical information of the player to classify the strokes. The player is modeled using a color histogram and tracked across the video using histogram back projection. The binarized (segmented) output of the tracker is skeletonized and the gradient information of the skeleton is extracted to form a feature vector. A three class SVM classifier is then used to classify the stroke to be a Forehand, Backhand or Neither. We evaluated the performance of our approach with real world datasets and have obtained promising results. Finally, the proposed approach is real time and can be used with live tennis broadcasts.