Action detection of volleyball using features based on clustering of body trajectories

For creating new tactics of sports like volleyball, the analysis of player motion in real games becomes more and more important. However, since motion data needed for the analysis is captured by human observation currently, an automatic capturing system from video camera is highly expected to gather many useful data easily. This paper proposes an action detection algorithm of volleyball players using motion features based on clustering and aggregation of body trajectories. Since the body trajectories of arms and legs are similar, the clustering utilizes shape, location and density of their trajectories. Furthermore, the clustered feature values are aggregated by means of their mean and variance. Experimental results by using the motion detection system based on the proposed algorithm show that it averagely attains 0.9539 AUC of the ROC curve for the detection of four basic motions (block, receive, spike and toss) from the volleyball game video captured by high-definition cameras. This is 0.014775 higher than conventional methods.