Human Action Recognition Method Based on Two-flow Skeleton Information
Aiming at the problems of poor robustness and low recognition rate of current human action recognition algorithms based on two-dimensional images,a two-stream human action recognition algorithm based on convolutional neural network and graph convolutional neural network was proposed to extract the temporal and spatial features of human action recognition from human skeleton information.Firstly,the spatial and temporal graph of skeleton information is constructed,and the graph convolution network with attention mechanism is used to extract the temporal and spatial characteristics of skeleton information.Secondly,the skeleton information action graph is constructed,and the features extracted from the convolutional neural network are used as the time and space features of the features extracted from the spatio-temporal graph convolutional network.Finally,the two-stream networks are fused to form a human action recognition algorithm based on dual flow and attention mechanism.The proposed algorithm enhances the representation ability of skeleton information and effectively improves the recognition accuracy of human movements.It achieves good results on the NTU-RGB+ D60 data set,and the recognition rates of Cross-Subject and Cross-View are 86.5%and 93.5%,respectively,which is a certain improvement compared with other similar algorithms.
国家科技期刊平台
NSTL
万方数据
维普
