BST: Badminton Stroke-type Transformer for Skeleton-based Action Recognition in Racket Sports

TL;DR

This paper introduces BST, a transformer-based model that classifies badminton stroke types using skeletal, shuttlecock, and court data, outperforming previous methods on multiple datasets.

Contribution

The paper presents a novel approach combining data extraction and a transformer model for improved badminton stroke classification.

Findings

BST outperforms previous state-of-the-art on ShuttleSet.

Effective use of shuttlecock trajectory improves action recognition.

Method generalizes to tennis dataset TenniSet.

Abstract

Badminton, known for having the fastest ball speeds among all sports, presents significant challenges to the field of computer vision, including player identification, court line detection, shuttlecock trajectory tracking, and player stroke-type classification. In this paper, we introduce a novel video clipping strategy to extract frames of each player's racket swing in a badminton broadcast match. These clipped frames are then processed by three existing models: one for Human Pose Estimation to obtain human skeletal joints, another for shuttlecock trajectory tracking, and the other for court line detection to determine player positions on the court. Leveraging these data as inputs, we propose Badminton Stroke-type Transformer (BST) to classify player stroke-types in singles. To the best of our knowledge, experimental results demonstrate that our method outperforms the previous state-of-the-art on the largest publicly available badminton video dataset (ShuttleSet), another badminton dataset (BadmintonDB), and a tennis dataset (TenniSet). These results suggest that effectively leveraging ball trajectory is a promising direction for action recognition in racket sports.