Human skeleton action recognition based on feature-enhanced spatiotemporal graph convolutional network

In recent years, deep learning-based human skeleton action recognition technology has seen significant development and application in areas such as human-computer interaction, security monitoring, and rehabilitation medicine. To address the issue of insufficient exploitation of spatiotemporal features in skeleton action recognition, this paper designs a Spatiotemporal Graph Convolutional Network with feature enhancement. This network enhances features from the perspectives of nodes, space, and time to improve the accuracy of skeleton action recognition. Firstly, by connecting graphs of adjacent frames to construct local spatiotemporal graphs, the network achieves a more comprehensive capture of spatial and temporal dependencies. Next, in the spatial graph convolution operations, self-attention mechanisms and channel attention modules are used to learn separate topological structures for different time frames, allowing the model to capture complex changes in human body structure across various time frames. Finally, in the temporal convolution stage, the use of multi-scale convolutional kernels enhances the perception of features across different temporal domains. This human skeleton action recognition model demonstrates efficient spatiotemporal feature extraction capabilities and excellent performance.