Human Action Recognition Method Based on Multi-Attention Mechanism and Spatiotemporal Graph Convolution Networks

Abstract

<p indent="0mm">Human action recognition has become a challenging task in computer vision because it is difficult to combine spatiotemporal information. A multi-attention spatiotemporal graph convolution network is proposed. The core idea is to construct a connected graph according to the time series information and natural connection of human skeleton, and use the spatiotemporal graph convolution network with multi-attention mechanism to automatically learn spatial and temporal features and optimize the connected graph to realize prediction. Graph attention module is introduced, the topological structure of the graph constructed by the model will be optimized with the process of network training after initialization, then the topological structure which is more suitable for expressing human actions will be obtained. In addition, the channel attention module is added to make the network pay more attention to the important channel information, so as to extract the features of describing actions more effectively. A large number of experiments are carried out on the recognized large datasets: NTU-RGDB and Kinectics, which show that the method has higher recognition accuracy.