用单人训练模型分析双人羽毛球，解决追踪难题
Bridging the Gap: Doubles Badminton Analysis with Singles-Trained Models
- 用ViT-Pose提取关键点，ST-GCN结合对比学习建模
- 自研多目标追踪算法解决高速重叠运动导致的身份切换问题
- 首次实现基于姿态的双打击球识别，为双打分析奠基
羽毛球是世界上最快速的球类运动之一。尽管双打比赛在国际赛事中比单打更常见，但以往研究多集中于单打，因数据获取困难和多人追踪挑战。为此，我们设计了一种将单打训练模型迁移至双打分析的方法。利用ViT-Pose从ShuttleSet单打数据集提取关键点，并通过基于ST-GCN的对比学习框架进行嵌入。为提升追踪稳定性，引入自研多目标追踪算法，有效解决高速重叠移动引发的身份切换问题。最后，采用Transformer分类器根据学习到的嵌入判断击球发生。结果表明，可将基于姿态的击球识别扩展至双打场景，拓展了分析能力。本工作为构建双打专用数据集奠定基础，推动对这一主流但研究不足的比赛形式的理解。
原文摘要 · Abstract (English)
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.
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