Action recognition algorithm based on multi-scale and multi-branch features

Aiming at the problems of insufficient feature extraction,incompleteness and low recognition accuracy in action recognition based on human skeleton sequence,a action recognition model based on multi -branch feature and multi-scale spatio-temporal feature is proposed in this paper. Firstly,the original data are enhanced by the combination of various algorithms. Secondly,the multi-branch feature input form is improved to multi-branch fusion feature information,which is input into the network,respectively. After a certain depth of network modules,it is fused together. Then,a multi-scale spatio-temporal convolution module is constructed as the basic module of the network to extract multi-scale spatio-temporal features. Finally,the overall network model is constructed to output action categories. The experimental results show that the recognition accuracy on Cross-subject and Cross-view of NTU RGB-D 60 data set is 89. 6% and 95. 1%,and the recognition accuracy on Cross-subject and Cross-setup of NTU RGB-D 120 data set is 84. 1% and 86. 0%,respectively. Compared with other algorithms,the more diversified and multi-scale action features are extracted,and the recognition accuracy of action categories is improved to a certain extent.