Data analysis in net sports, such as badminton, is becoming increasingly important. This research aims to analyze data so that players can gain an advantage in the fast rally development of badminton matches. We investigate the novel task of predicting future shuttle trajectories in badminton match videos and propose a method that uses shuttle and player position information. In an experiment, we detected players from match videos and trained a time-sequence model. The proposed method outperformed baseline methods that use only the shuttle position information as the input and other methods that use time-sequence models.
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