DG-STGCN: Dynamic Spatial-Temporal Modeling for Skeleton-based Action Recognition

• URL: http://arxiv.org/abs/2210.05895v1
• Date: Wed, 12 Oct 2022 03:17:37 GMT
• Title: DG-STGCN: Dynamic Spatial-Temporal Modeling for Skeleton-based Action Recognition
• Authors: Haodong Duan, Jiaqi Wang, Kai Chen, Dahua Lin
• Abstract summary: We propose a new framework for skeleton-based action recognition, namely Dynamic Group Spatio-Temporal GCN (DG-STGCN) It consists of two modules, DG-GCN and DG-TCN, respectively, for spatial and temporal modeling. DG-STGCN consistently outperforms state-of-the-art methods, often by a notable margin.
• Score: 77.87404524458809
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph convolution networks (GCN) have been widely used in skeleton-based action recognition. We note that existing GCN-based approaches primarily rely on prescribed graphical structures (ie., a manually defined topology of skeleton joints), which limits their flexibility to capture complicated correlations between joints. To move beyond this limitation, we propose a new framework for skeleton-based action recognition, namely Dynamic Group Spatio-Temporal GCN (DG-STGCN). It consists of two modules, DG-GCN and DG-TCN, respectively, for spatial and temporal modeling. In particular, DG-GCN uses learned affinity matrices to capture dynamic graphical structures instead of relying on a prescribed one, while DG-TCN performs group-wise temporal convolutions with varying receptive fields and incorporates a dynamic joint-skeleton fusion module for adaptive multi-level temporal modeling. On a wide range of benchmarks, including NTURGB+D, Kinetics-Skeleton, BABEL, and Toyota SmartHome, DG-STGCN consistently outperforms state-of-the-art methods, often by a notable margin.

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