Temporal-difference Adaptive Graph Convolutional Network for Skeleton-based Action Recognition

Abstract

Abstract In action recognition based on the skeleton, graph convolutional networks (GCNs) have shown great superiority based on the design of making the skeleton in the video a spatiotemporal map and extracting features from the spatiotemporal map. However, the topology of the skeleton in GCN-based methods is pre-designed according to prior knowledge, which limits the capacity of the network to learn high-level topology about the skeleton. To improve this deficiency, we design a temporal-difference adaptive graph convolutional network (TDA-GCN) that can learn the potential topology of the human skeleton from the input data, which is augmented using the channel attention module. Experiments show that TDA-GCN achieves state-of-the-art performance on two large-scale skeleton datasets, NTU-RGBD and Kinetics-Skeleton.