Human Action Recognition Method Based on Graph Attention Network and Directed Graph Neural Network
The graph convolutional neural network based on human skeleton data is not easily affected by background environmental noise and has strong robustness,which has become a research focus in the field of human action recognition at present.However,this network assigns the same weight to different neighborhoods in the same order,which limits its ability to capture spatial information correlations.To this end,a graph attention network weighted sum is introduced to sum the features of adjacent nodes,allowing each node to assign different weights based on its adjacent features to enhance feature extraction and learning effectiveness.At the same time,in order to solve the problem of representing the skeleton as an undirected graph,only the relationship between adjacent nodes or edges can be determined,which limits the ability to cap-ture dependency relationships between nodes or edges.Introducing directed graph convolution,utilizing the feature information of first-order and second-order adjacent nodes for graph convolution,not only preserves the directional features of the directed graph,but also expands the perceptual domain of graph convolution,thereby extracting more features.The experiment shows that the proposed method can effectively im-prove the accuracy of action recognition.
万方数据
维普
