Body Prior Guided Graph Convolutional Neural Network for Skeleton-Based Action Recognition

Abstract

Graph Convolutional Network (GCN) has achieved high success in the skeleton-based human action recognition task by modeling the human skeleton as a graph. However, it remains a problem for GCN-based methods to learn distinctive action features from a limited number of training samples. Via taking full advantage of the body prior knowledge, this paper presents a Body Prior Guided Graph Convolutional Network (BPG-GCN) to jointly meet the demand for large-scale training data and effective model architecture. Unlike standard GCN-based methods, our BPG-GCN additionally involves both Body Prior Guided Drop (BPGD) and Body Prior Guided Attention (BPGA) modules. Specifically, the BPGD module generates diverse augmented skeleton sequences by selectively dropping spatial-temporal skeleton joints. Moreover, the BPGA module combines body structure and attention mechanism to learn distinctive action features for specific body parts. Extensive experiments on NTU-60 and NW-UCLA datasets consistently verify the effectiveness of our proposed BPG-GCN by outperforming state-of-the-art GCN-based methods. Our code is publicly available at https://github.com/519542630/BPG-GCN.