Abstract: Graph convolutional neural network(GCN) has achieved good results in the field of human action recognition based on 3D skeleton.However,in most of the existing GCN methods,the construction of the behavior diagram is based on the manual setting of the physical structure of the human body.In the training stage,each graph node can only establish the connection accor-ding to the manual setting,which cannot perceive new connections between bone nodes during action,resulting in the unreasonable and inflexible topology of the graph.The shifted graph convolutional neural network(Shift-GCN) makes the receptive field more flexible by changing its structure,and achieves satisfied results in the global shift angle.In order to tackle the above pro-blems of graph structure,an adaptive shift graph convolutional neural network(AS-GCN) is proposed to make up for the above shortcomings.AS-GCN draws on the idea of shifted graph convolutional neural network,and proposes to use the characteristics of each human action to guide the graph network to perform shift operation,so as to select the nodes that need to expand the receptive field as accurately as possible.On the general skeleton-based action recognition dataset NTU-RGBD,the AS-GCN is verified by extensive experiments under the premise of whether the skeleton has physical relationship constraints or not.Compared with the existing advanced algorithms,the accuracy of action recognition of AS-GCN is improved by 12% and 4.84% respectively in CV and CS angles on average with skeleton physical constraints.While under the condition of no skeleton physical constraint,the average improvement is 20% and 14.49% in CV and CS angles,respectively.

Key words: Skeleton-based action classification, Graph convolutional neural network, Action recognition, Adaptive shift