Towards Structured Analysis of Broadcast Badminton Videos

TL;DR

This paper presents an end-to-end framework for automatic analysis and attribute tagging of broadcast badminton videos, enabling large-scale data mining without special equipment.

Contribution

It introduces a novel method for segmenting points, tracking players, and recognizing strokes in badminton videos using standard broadcast footage, without additional sensors.

Findings

Achieved 95.44% point segmentation accuracy

Attained 97.38% player detection score (mAP@0.5)

Stroke segmentation edit score of 80.48%

Abstract

Sports video data is recorded for nearly every major tournament but remains archived and inaccessible to large scale data mining and analytics. It can only be viewed sequentially or manually tagged with higher-level labels which is time consuming and prone to errors. In this work, we propose an end-to-end framework for automatic attributes tagging and analysis of sport videos. We use commonly available broadcast videos of matches and, unlike previous approaches, does not rely on special camera setups or additional sensors. Our focus is on Badminton as the sport of interest. We propose a method to analyze a large corpus of badminton broadcast videos by segmenting the points played, tracking and recognizing the players in each point and annotating their respective badminton strokes. We evaluate the performance on 10 Olympic matches with 20 players and achieved 95.44% point segmentation accuracy, 97.38% player detection score (mAP@0.5), 97.98% player identification accuracy, and stroke segmentation edit scores of 80.48%. We further show that the automatically annotated videos alone could enable the gameplay analysis and inference by computing understandable metrics such as player's reaction time, speed, and footwork around the court, etc.