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Volleyball game analysis is essential for extracting useful information in various applications such as quantitative game and TV content. Detecting the start scene of each round of volleyball game is an important task eager to be solved for achieving automatic volleyball game analysis. To achieve high success rate and real-time process, crucial problem including effect of background noise, small difference between motions and redundant calculation of pixel likelihood need to be solved. This paper proposes a 3D ball motion and related position feature based real-time start scene detection for volleyball analysis. Template update based color likelihood estimation method is proposed to eliminate the ball-like background noise. The related position between ball and server feature is utilized to distinguish scenes with similar ball motion. Motion estimation is combined to reduce the redundant calculation for GPU acceleration. Experimental results are based on multi-view HDTV video sequences (2014 Inter High School Men’s Volleyball Games, Japan). On the GPU device GeForce GTX 1080Ti, we achieve real-time processing in 60 fps sequences. The time consumption of feature extraction scheme is 16.37 ms /frame with a detection recall rate of 97.67% and a precision rate of 100% in average.

3D Ball Motion and Relative Position Feature Based Real-time Start Scene Detection for Volleyball Game Analysis on GPU