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Volleyball video analysis is important for developing applications such as player evaluation system or tactic analysis system. Among its dierent topics, player action recognition is the key part for understanding player’s behavior. Most existing research focused on the discrimination between different actions. The quality of an action has received little attention so far, even though the action quality potentially provides lots of useful information for volleyball analysis. Most action recognition works cannot work well due to the high similarity in different qualitative actions and appearance variation caused by target human change. This paper proposed a ball motion state and abrupt pose features based qualitative action recognition for volleyball player. Ball motion state feature is to evaluate the action quality through the ball transition caused by the action, which evaluates the return ball quality and indicates the motion quality. Abrupt pose feature is to represent the abrupt shape difference among different qualitative actions, which overcomes the high similarity in different qualitative actions. Experiments are conducted on game videos from the Seminal and Final Game of 2014 Japan Inter High School Games of Men’s Volleyball in Tokyo Metropolitan Gymnasium. The experiments show the result accuracy achieves 91.76%, 13.72% improvement than conventional work.

Ball Motion State and Abrupt Pose Features based Player Qualitative Action Recognition for Volleyball Game Analysis