Motion state detection based prediction model for body parts tracking of volleyball players

Among sports analysis, tracking of athletes’ body parts becomes a popular theme. Marking positions of body parts on the videos which contributes to TV contents and concrete motion capture of athletes which helps promotion of sports technology make sports analysis a commercially-viable research theme. This paper proposes motion state detection based prediction model to predict the near future motions of players’ arms, band-width sobel likelihood model to observe the shape of human body parts and cluster scoring based estimation to avoid huge error. The motion state detection based prediction model can realize the tracking of players’ high-speed and random motions without templates. The band-width sobel likelihood model can fully express unique shape features of target player’s body parts. And the cluster scoring based estimation utilizes k-means cluster method to divide particle into 3 clusters and evaluate each cluster by scoring in order to prevent huge error from similar noises. The experiments are based on videos of the Final Game of 2014 Japan Inter High School Games of Men’s Volleyball in Tokyo. The tracking success rate reached over 97% for lower body and over 80% for upper body, achieving average 64% improvement of hands compared to conventional work [1].