App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 6

Adaptive spatio-temporal graph convolutional network for skeleton-based action recognition

Xu Hong1
Lyu Kai2
Yuan Liang1,2,3
1. School of Software, Xinjiang University, Ürümqi 830091, China
2. School of Mechanical Engineering, Xinjiang University, Ürümqi 830046, China
3. ICCI, Shanghai Jiao Tong University, Shanghai 200240, China
DOI: 10.19734/j.issn.1001-3695.2025.09.0410

Abstract

Most skeleton-based action recognition methods model spatial relationships using a predefined skeletal topology. However, this pre-defined topology suffers from inherent limitations, as its static and shared nature restricts the flexibility of feature extraction. To address this issue, this paper proposed an Adaptive Spatio-Temporal Graph Convolutional Network (AST-GCN) for skeleton-based action recognition. The network consists of an Adaptive Spatial Graph Convolution (AS-GC) and an Adaptive Temporal Graph Convolution (AT-GC) . The AS-GC replaces the fixed topology with multiple learnable adjacency matrices to adaptively extract spatial features of poses. The AT-GC models the temporal dimension at multiple levels, enabling detailed learning of dynamic information across different time segments through learnable adjacency matrices. AST-GCN achieves an accuracy of 92.9% (X-Sub) and 96.9% (X-View) on the large-scale dataset NTU-RGB+D 60, and 89.7% (X-Sub) and 91.0% (X-Set) on NTU-RGB+D 120. Its performance surpasses that of existing mainstream methods, demonstrating the effectiveness of the model.

Foundation Support

国家自然科学基金资助项目(62501517,52275003)
中央高校基本科研业务费专项资金资助项目(buctrc202105)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.09.0410
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 6

Publish History

[2026-02-05] Accepted Paper

Cite This Article

徐洪, 吕凯, 袁亮. 基于自适应时空图卷积的骨骼行为识别方法 [J]. 计算机应用研究, 2026, 43 (6). (2026-02-25). https://doi.org/10.19734/j.issn.1001-3695.2025.09.0410. (Xu Hong, Lyu Kai, Yuan Liang. Adaptive spatio-temporal graph convolutional network for skeleton-based action recognition [J]. Application Research of Computers, 2026, 43 (6). (2026-02-25). https://doi.org/10.19734/j.issn.1001-3695.2025.09.0410. )

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  • Application Research of Computers Monthly Journal
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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