BST: Badminton Stroke-type Transformer for Skeleton-based Action Recognition in Racket Sports
This work addresses action recognition in racket sports, specifically for badminton analysis, but is incremental as it builds on existing models for pose estimation and tracking.
The paper tackles the problem of classifying player stroke-types in badminton singles by introducing a novel video clipping strategy and the Badminton Stroke-type Transformer (BST), which outperforms previous state-of-the-art methods on multiple datasets including ShuttleSet, BadmintonDB, and TenniSet.
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.