Badminton Action Recognition Using Skeleton Data and Optical Flow

The ability to analyze actions in videos is crucial for the automatic understanding of sports. With advancements in action recognition, precise video analysis has become increasingly feasible. Although extensive research has been conducted in the field of action recognition, relatively few studies focus on badminton and broadcast sports videos. In this paper, we propose a two-stream architecture for classifying badminton actions. Our approach leverages two key features-optical flow and skeleton data-as inputs to the two-stream architecture. Given the complexity of badminton movements and techniques, these features are chosen to effectively capture human actions. The extracted features are then processed using a combination of VGG and bi-directional long short-term memory (Bi-LSTM) models for training and classification. Specifically, VGG, a convolutional neural network, is employed in the optical flow stream for feature extraction, while Bi-LSTM is utilized in both streams to capture the dynamic temporal information of video sequences. Our proposed two-stream architecture achieves an accuracy of 94.3% on our badminton dataset, demonstrating its effectiveness in analyzing broadcast sports videos.