Automated Hit-frame Detection for Badminton Match Analysis

TL;DR

This paper presents an automated deep learning-based system for detecting hit-frames in badminton videos, enabling detailed performance analysis and strategic insights with high accuracy.

Contribution

It introduces a comprehensive automated pipeline for badminton match analysis, including rally-wise video trimming, keypoints detection, and hit-frame identification, advancing sports analytics technology.

Findings

99% accuracy in shot angle recognition

Over 92% accuracy in shuttlecock direction prediction

Effective rally-wise video trimming and hit-frame detection

Abstract

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.