Dynamic Hypergraph Convolutional Networks for Skeleton-Based Action Recognition
This work addresses limitations in graph convolutional networks for skeleton-based action recognition, offering an incremental improvement for computer vision applications.
The authors tackled the problem of skeleton-based action recognition by proposing a dynamic hypergraph convolutional network (DHGCN) to better represent motion information, achieving competitive performance on datasets like Kinetics-Skeleton 400 and NTU RGB+D.
Graph convolutional networks (GCNs) based methods have achieved advanced performance on skeleton-based action recognition task. However, the skeleton graph cannot fully represent the motion information contained in skeleton data. In addition, the topology of the skeleton graph in the GCN-based methods is manually set according to natural connections, and it is fixed for all samples, which cannot well adapt to different situations. In this work, we propose a novel dynamic hypergraph convolutional networks (DHGCN) for skeleton-based action recognition. DHGCN uses hypergraph to represent the skeleton structure to effectively exploit the motion information contained in human joints. Each joint in the skeleton hypergraph is dynamically assigned the corresponding weight according to its moving, and the hypergraph topology in our model can be dynamically adjusted to different samples according to the relationship between the joints. Experimental results demonstrate that the performance of our model achieves competitive performance on three datasets: Kinetics-Skeleton 400, NTU RGB+D 60, and NTU RGB+D 120.