Bridging the Gap: Doubles Badminton Analysis with Singles-Trained Models

TL;DR

This paper introduces a novel approach to analyze doubles badminton by transferring models trained on singles data, utilizing pose estimation, contrastive learning, and multi-object tracking to enable shot recognition in doubles matches.

Contribution

It presents a method for adapting singles-trained models to doubles analysis, addressing data scarcity and tracking challenges in doubles badminton.

Findings

Feasibility of extending pose-based shot recognition to doubles

Development of a multi-object tracking algorithm for doubles

Foundation for doubles-specific badminton datasets

Abstract

Badminton is known as one of the fastest racket sports in the world. Despite doubles matches being more prevalent in international tournaments than singles, previous research has mainly focused on singles due to the challenges in data availability and multi-person tracking. To address this gap, we designed an approach that transfers singles-trained models to doubles analysis. We extracted keypoints from the ShuttleSet single matches dataset using ViT-Pose and embedded them through a contrastive learning framework based on ST-GCN. To improve tracking stability, we incorporated a custom multi-object tracking algorithm that resolves ID switching issues from fast and overlapping player movements. A Transformer-based classifier then determines shot occurrences based on the learned embeddings. Our findings demonstrate the feasibility of extending pose-based shot recognition to doubles badminton, broadening analytics capabilities. This work establishes a foundation for doubles-specific datasets to enhance understanding of this predominant yet understudied format of the fast racket sport.