Abstract: In recent years, graph convolution network has attracted the attention of many researchers due to its excellent performance in the field of skeleton action recognition. However, most graph convolution can only aggregate node information, ignoring the difference between the features of the central node and adjacent nodes. Therefore, a central difference adaptive graph convolution network MRFAM-CDAGC based on multiple receptive fields attention mechanism is proposed. It not only adaptively aggregates the information of associated nodes in the graph topology of the central node, but also merge the local motion information between adjacent nodes and aggregate the gradient characteristics of the central node. The attention module with multiple receptive fields is added to make the model focus on the information of more discriminative joints and frames, so as to improve the accuracy of model recognition. Under the two baselines of NTU-RGB-D data sets, the accuracy rates of the model reach 89.1% and 96.0% respectively. The universality of the model is reflected in the dynamics of large-scale data set, which verifies the superiority of the algorithm in extracting spatiotemporal features and capturing global context information.

Key words: behavior recognition, central difference adaptive graph convolution, attention mechanism, skeleton recognition