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.

翻译：体育专业人员始终承受着高水平表现的压力，而体育分析能够通过自动化工具减少教练和运动员的手动工作，并系统地评估其表现，从而为其提供支持。本研究旨在推进羽毛球运动分析，利用现代深度学习技术从比赛视频中自动检测击球帧。击球帧包含的数据随后可用于合成运动员的击球动作和场上移动，以及用于其他下游应用，如分析训练任务和比赛策略。本研究提出的方法包括多项自动化流程，如按回合视频剪辑、球员及球场关键点检测、羽毛球飞行方向预测和击球帧检测。研究中，我们在视频剪辑的击球角度识别上达到了99%的准确率，在利用球员关键点序列进行羽毛球飞行方向预测上实现了超过92%的准确率，并报告了按回合视频剪辑和击球帧检测的评估结果。