Spike and toss event detection method based in a volleyball game video

This paper presents a method for detecting spike and toss events in a volleyball game video, addressing the time-consuming task for a less-experienced analyst. The approach combines analyzing ball trajectory and detecting player movements near the net. It uses TrackNet to detect and track the high-speed ball, and deep learning to detect player regions. The height of the ball is used to identify the toss event from peaks in the ball’s <i>y</i>-coordinate, and the sizes of the player regions are monitored to detect the spike event. The method achieved an F1-score of 0.82 in volleyball video evaluation, and it indicates high accuracy and potential to streamline the volleyball analysis.