Abstract: Human action recognition achieves the identification of human behaviors by analyzing spatiotemporal features in vi-deos.As one of the important research topics in the field of computer vision,its efficient and accurate recognition performance has demonstrated wide application value in various scenarios such as human-computer interaction and intelligent security.Graph Convolutional Networks(GCNs),owing to their significant advantages in modeling human skeletal topology,have become a mainstream method for action recognition tasks.However,existing approaches generally adopt a unified modeling of the entire skeleton structure,overlooking the hierarchical characteristics of the human body composed of multiple functional regions.This limitation restricts model performance in complex action recognition tasks.To address these,this paper proposes a Topology-informed Multi-layer Graph Convolutional Network(TMGCN).The model employs a multi-branch architecture to partition and model the human skeleton,effectively capturing spatial dependencies between skeletal nodes.Additionally,it introduces a Topology Perception Unit(TPU) to extract and integrate topological features during graph convolution,enhancing the model's representation capability for skeletal topology.Experimental results based on NTU-RGB+D dataset show that TM-GCN has achieved excellent performance in human skeletal action recognition tasks,and effectively improved the accuracy of action recognition.

Key words: Action recognition, Skeleton modality, Graph convolutional network, Topology-aware, Computer vision