Feature Fusion Network for Skeleton-based Action Recognition

With the increasing demand for intelligence in human-computer interaction, secu-rity monitoring, intelligent nursing, and sports analysis, the development of hu-man skeletal behavior recognition technology has attracted more attention. How-ever, current methods based on human skeleton recognition encounter a balance issue between model complexity and accuracy, and struggle to comprehensively extract the required features. To address these challenges, this study proposes a Feature Fusion Network based on ST-GCN as the backbone network, which achieves comprehensive and detailed feature extraction through multiple feature fusion operations within the network. The parameter count of FFN is only 3.35 million. It achieves accuracies of 94.24 and 98.30 on the 2D skeletal data of the NTU RGB_D 60 dataset using the cross-subject and cross-view partition cri-teria, respectively. On the NTU RGB_D 120 dataset, it achieves accuracies of 87.31 and 90.95 using the cross-subject and cross-setup partition criteria, re-spectively, representing a state-of-the-art performance in the field of deep learning for skeleton action recognition.