Skeleton action recognition using Two-Stream Adaptive Graph Convolutional Networks

Using skeleton data to classify human behavior has been received a lot of attention and various technologies have been developed because it can be utilized in various domains. Among them, Graph Convolutional Networks (GCN) shows high accuracy in action recognition through the effective extraction of features from graph data. GCN has been further developed and two stream adaptive graph convolution networks (2s-AGCN) are made. This model utilizes bone data as well as spatial and temporal information of joints to compensate for the problems of conventional GCN limited to local region. Although 2s-AGCN show the highest accuracy among existing technologies, new approaches using CNN, LSTM and auto encoder continue to emerge and present several methods. In this paper, we verify if 2s-AGCN show higher accuracy than recent CNN-LSTM networks and compare structures and features of their models.