Dynamic Spatial-temporal Hypergraph Convolutional Network for Skeleton-based Action Recognition
Skeleton-based action recognition relies on the extraction of spatial-temporal topological information. Hypergraphs can establish prior unnatural dependencies for the skeleton. However, the existing methods only focus on the construction of spatial topology and ignore the time-point dependence. This paper proposes a dynamic spatial-temporal hypergraph convolutional network (DST-HCN) to capture spatial-temporal information for skeleton-based action recognition. DST-HCN introduces a time-point hypergraph (TPH) to learn relationships at time points. With multiple spatial static hypergraphs and dynamic TPH, our network can learn more complete spatial-temporal features. In addition, we use the high-order information fusion module (HIF) to fuse spatial-temporal information synchronously. Extensive experiments on NTU RGB+D, NTU RGB+D 120, and NW-UCLA datasets show that our model achieves state-of-the-art, especially compared with hypergraph methods.
Code (0)
등록된 구현이 없습니다.
Tasks
Action RecognitionSkeleton Based Action RecognitionSimilar Papers 제목 키워드 기반
Dynamic Hypergraph Convolutional Networks for Skeleton-Based Action Recognition
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 skele…
Action RecognitionSkeleton Based Action RecognitionAutoregressive Adaptive Hypergraph Transformer for Skeleton-based Activity Recognition
Extracting multiscale contextual information and higher-order correlations among skeleton sequences using Graph Convolutional Networks (GCNs) alone is inadequate for effective action classification. Hypergraph convolutio…
Action ClassificationActivity RecognitionSTDHL: Spatio-Temporal Dynamic Hypergraph Learning for Wind Power Forecasting
Leveraging spatio-temporal correlations among wind farms can significantly enhance the accuracy of ultra-short-term wind power forecasting. However, the complex and dynamic nature of these correlations presents significa…
DecoderEvolving Skeletons: Motion Dynamics in Action Recognition
Skeleton-based action recognition has gained significant attention for its ability to efficiently represent spatiotemporal information in a lightweight format. Most existing approaches use graph-based models to process s…
Action RecognitionHypergraph representationsSkeleton Based Action RecognitionHFGCN:Hypergraph Fusion Graph Convolutional Networks for Skeleton-Based Action Recognition
In recent years, action recognition has received much attention and wide application due to its important role in video understanding. Most of the researches on action recognition methods focused on improving the perform…
Action RecognitionRelation ClassificationSkeleton Based Action RecognitionVideo Understanding