2021/12/20 by Jinfeng Wei, Wei, Jinfeng, Yunxin Wang +9 · 6 citations
Computer Science · Engineering · Mathematics · Medicine · #Action recognition #Artificial intelligence #Class (philosophy) #Combinatorics #Computer Vision and Pattern Recognition (cs.CV) #Computer science #FOS: Computer and information sciences #Graph #Human Pose and Action Recognition #Hypergraph #Mathematics #Medical Imaging and Analysis #Pattern recognition (psychology) #RGB color model #Skeleton (computer programming) #Stroke Rehabilitation and Recovery #Theoretical computer science #Topology (electrical circuits) #cs.CV
paper · pdf · doi:10.48550/arxiv.2112.10570
published in arXiv (Cornell University) (Cornell University) · 12 pages, 6 figures
arxiv created 2021/12/20 · openalex publication_date 2021/12/20 · arxiv updated 2021/12/21 · openalex created_date 2022/05/05 · openalex updated_date 2026/08/05
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.