Action Recognition Using Attention-Joints Graph Convolutional Neural Networks

Human skeleton contains significant information about actions, therefore, it is quite intuitive to incorporate skeletons in human action recognition. Human skeleton resembles to a graph where body joints and bones mimic to graph nodes and edges. This resemblance of human skeleton to graph structure is the main motivation to apply graph convolutional neural network for human action recognition. Results show that the discriminant contribution of different joints is not equal for different actions. Therefore, we propose to use attention-joints that correspond to joints significantly contributing to the specific actions. Features corresponding to only these attention-joints are computed and assigned as node features of the graph. In our method, node features (also termed as attention-joint features) include the i) distances of attention-joints from the center-of-gravity of human body, ii) distances between adjacent attention-joints and iii) joints flow features. The proposed method gives a simple but more efficient representation of skeleton sequences by concatenating more relative distances and relative coordinates to other joints. The proposed methodology has been evaluated on single image Stanford 40-Actions dataset, as well as on temporal skeleton-based action recognition PKU-MDD and NTU-RGBD datasets. Results show that this framework outperforms existing state-of-the-art methods.