Local and Non-local Context Graph Convolutional Networks for Skeleton-Based Action Recognition.

Graph convolutional networks (GCNs) for skeleton-based action recognition have achieved considerable progress recently. However, there are still two unresolved shortages. One is that the input data lacks high-level motion information of discriminant features. The other is that the access to the long-range action features is limited by the local sampling scale. In this work, we propose a new model called local and non-local context graph convolutional networks (LnLC-GCN). The first innovation is a motion enhanced graph containing high-level motion information which is served as the multi-stream input. Secondly, to overcome the limitations of local receptive field, we present a local and non-local context module based on the global context mechanism. Moreover, we use two optimization strategies of front-end fusion and non-local context feedback to further improve the accuracy of LnLC-GCN. For validation of the performance, numerous experiments were deployed on three public datasets, NTU-RGB+D 60 & 120 and Kinetics-Skeleton, strongly demonstrating that our approach achieves state-of-the-art.