S $ ^3 $ GCN: Sport Scoring Siamese Graph Convolution Network

Temporal sequences of human body key points provide detailed motion information, serving as a crucial foundation for human action analysis. Existing public methods and datasets predominantly focus on action category estimation, lacking a comprehensive evaluation of sport scoring. In this work, we propose a novel model of sport scoring called Sport Scoring Siamese Graph Convolution Network S3GCN, which surpasses the constraints inherent in prior methods by implicitly capturing nuanced differences between teacher pose and student pose. In a Few-shot dataset, Taichi, it achieves a benchmark level of performance through spacial and temporal augmentation with comprehensive ablation experiments. Furthermore, our approach outperforms the original model on classification, including NTU-RGB-D and Taichi classification datasets.1https://github.com/divided7/SSSGCN