  Sports professionals constantly under pressure to perform at the highest
level can benefit from sports analysis, which allows coaches and players to
reduce manual efforts and systematically evaluate their performance using
automated tools. This research aims to advance sports analysis in badminton,
systematically detecting hit-frames automatically from match videos using
modern deep learning techniques. The data included in hit-frames can
subsequently be utilized to synthesize players' strokes and on-court movement,
as well as for other downstream applications such as analyzing training tasks
and competition strategy. The proposed approach in this study comprises several
automated procedures like rally-wise video trimming, player and court keypoints
detection, shuttlecock flying direction prediction, and hit-frame detection. In
the study, we achieved 99% accuracy on shot angle recognition for video
trimming, over 92% accuracy for applying player keypoints sequences on
shuttlecock flying direction prediction, and reported the evaluation results of
rally-wise video trimming and hit-frame detection.
