Scene context-aware graph convolutional network for skeleton-based action recognition

Abstract

Skeleton-based action recognition methods commonly employ graph neural networks to learn different aspects of skeleton topology information However, these methods often struggle to capture contextual information beyond the skeleton topology. To address this issue, a Scene Context-aware Graph Convolutional Network (SCA-GCN) that leverages potential contextual information in the scene is proposed. Specifically, SCA-GCN learns the co-occurrence probabilities of actions in specific scenarios from a common knowledge base and fuses these probabilities into the original skeleton topology decoder, producing more robust results. To demonstrate the effectiveness of SCA-GCN, extensive experiments on four widely used datasets, that is, SBU, N-UCLA, NTU RGB + D, and NTU RGB + D 120 are conducted. The experimental results show that SCA-GCN surpasses existing methods, and its core idea can be extended to other methods with only some concatenation operations that consume less computational complexity.