Enhanced decoupling graph convolution network for skeleton-based action recognition

In skeleton-based action recognition, graph convolution networks have been widely applied and very successful. However, because graph convolution is a local operation with a small field of perception, it cannot investigate well for the connections between joints that are far apart in the skeleton graph. In addition, graph convolution makes all channels share the same adjacency matrix, which causes the topology learned to be the same among different channels, which limits the ability of graph convolution to learn topological information. In this paper, we propose an enhanced decoupling graph convolution network that effectively expands the perceptual field of the graph convolution by adding additional graphs, and the decoupled feature fusion mechanism increases its expressive power. In addition, we introduce an attention mechanism in the model to obtain the important elements in the whole feature map from both spatial and temporal dimensions simultaneously, so that the graph convolution can focus on the important elements more precisely and efficiently and suppress the influence of irrelevant elements on the model performance. To validate the effectiveness and advancedness of the proposed model, we conducted extensive experiments on three large datasets: NTU RGB+D 60, NTU RGB+D120 and Northwestern-UCLA. On the NTU RGB+D 60 dataset, the accuracy of our model archieves 91.6% and 96.5% on the two protocols.