S³GCN: 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 S<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>GCN)<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>, 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.<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>https://github.com/divided7/SSSGCN