Detection of Shot Information Using Footwork Trajectory and Skeletal Information of Badminton Players

Naoki Tanaka, Hidehiko Shishido, Masashi Suita, Takeshi Nishijima, Yoshinari Kameda, Itaru Kitahara

Abstract: As video analysis has become important for sports science, various research has been conducted. In badminton, while shot information is essential primary data for performance analysis, it has been input manually, which makes it difficult to give instant feedback onsite. Our research aims to automatically detect shot information from videos of badminton game. By applying video tracking, the player’s footwork trajectory and skeletal information are estimated. Based on the estimated information, the hit timing is detected using deep learning classification. The horizontal position of the hit point, which is useful for game analysis, is also detected from the player’s footwork trajectory around the hit timing.