Player pose analysis in tennis video based on pose estimation

The demand for sports video analysis is expanding. Sports video analysis is used to analyze their own play and play of opponent players, and visualize movement and ability of players. Scientific and objective analysis become possible by incorporating video analysis. It is desired to improve the competition level by feeding back the analysis results to the players. Therefore, in this research, in the case of feeding back the analysis result to the athlete, research purpose is to realize a method which can understand and evaluate the details of the form hitting the ball in detail. First, joint position coordinates are estimated from input RGB tennis images by a posture estimation method. The joint position coordinates in each frame at the time of shot are classified using unsupervised method and represented by BoW. The feature vector is designed by combining this with the shot position. The probability of shot success is predicted using this feature vector. By visualizing BoW of the shot with the high probability of success and the high failure probability, it is possible to extract and compare poses that are likely to appear in each case without giving correct labels.