Skeleton-based Double Human Interaction Recognition Based on Global Segmentation Graph Convolutional Network
Graph convolutional network(GCN)is widely used in interaction recognition,but the traditional graph convolutional network is not enough to learn node features.Especially in interaction,the features of each node often contain information of multiple people,resulting in inaccurate node features.In order to effectively extract the correlation features between the nodes between interaction behaviors and aggregate the feature information,a global segmentation graph convolution network(GS-GCN)is proposed,which consists of global segmentation graph convolution(GSGC)and hierarchical aggregation attention(HAA)modules.GSGC combines graph convolution(GCN)and global segmenta-tion graph(GS-Graph),treats two-person interaction recognition as global recognition,has multiple adjacency matrices,and extracts single-person feature and two-person global feature.In addition,the importance of single level and double level is different.In order to highlight the interactive information between interaction behaviors,the attention module of hierarchical aggregation(HAA)is introduced to highlight the more obvious semantic information in interaction behaviors.Through experiments on NTU-RGB+D,NTU-RGB+D 120 and SBU two-person interaction data sets,the validity of the model is verified,and it is obviously superior to other two-person interaction recognition methods.
human interaction recognitiongraph convolution networkadjacency matrixattention module
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