TemPose-TF-ASF: Two-Stage Bidirectional Stroke Context Fusion for Badminton Stroke Classification
This work addresses the problem of temporal context modeling for fine-grained sports analysis, providing a method that can be integrated into existing models to improve stroke classification accuracy.
The paper proposes TemPose-TF-ASF, a context-aware extension for badminton stroke classification that leverages bidirectional stroke-type information from adjacent strokes. It achieves consistent improvements in Accuracy and Macro-F1 over baselines on a large-scale dataset.
Accurate badminton stroke prediction is crucial for fine-grained sports analysis and tactical decision support. However, existing methods struggle to model rich temporal context. This paper introduces \emph{TemPose-TF-ASF (Adjacent-Stroke Fusion)}, a context-aware extension of \emph{TemPose}. It enhances stroke recognition by incorporating stroke-type information from both preceding and subsequent strokes. A two-stage training and inference strategy is adopted. Preliminary predictions from the baseline model are reused as estimated temporal context. These predictions guide the joint optimization of the \emph{ASF} module and the classifier. By explicitly modeling bidirectional temporal stroke dependencies, the proposed method can be seamlessly integrated into existing state-of-the-art models. Experiments on a large-scale badminton match dataset show consistent improvements over the baseline and its variants in terms of Accuracy and Macro-F1. Moreover, integrating \emph{ASF} into other advanced methods yields notable performance gains. These results demonstrate strong transferability and generalization capability.