Abstract
This paper presents two novel approaches for improving skeleton-based action recognition using Graph Convolutional Networks (GCN) and Convolutional Neural Networks (CNN). In the first approach, we combine GCN and CNN streams that process position and velocity features to improve classification accuracy. In the second approach, we use GCN as an embedding layer for support network CNN to extract features from skeleton data, which significantly improves recognition accuracy. Our experiments on the JHMDB dataset demonstrate that our approaches outperform state-of-the-art methods while using significantly fewer parameters. Additionally, we extended our evaluation to the Kinetics-400 dataset, where our methods showed comparable results with considerably lower model complexity. Our work contributes to the development of more efficient and robust action recognition models.