计算机应用研究2026，Vol.43Issue(5)：1594-1600,7.DOI:10.19734/j.issn.1001-3695.2025.09.0410
基于自适应时空图卷积的骨骼行为识别方法
Adaptive spatio-temporal graph convolutional network for skeleton-based action recognition
摘要
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 AST-GCN for skeleton-based action recognition.The network consists of an AS-GC and an AT-GC.The AS-GC replaced the fixed topology with multiple learnable adjacency matrices to adaptively extract spatial features of poses.The AT-GC modeled the temporal dimension at multiple levels,enabling detailed learning of dynamic information across different time segments through learnable adjacency matrices.AST-GCN achieved 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 ef-fectiveness of the model.关键词
行为识别/图卷积网络/自适应/时空拓扑Key words
action recognition/graph convolutional network/adaptive/spatio-temporal topology分类
信息技术与安全科学引用本文复制引用
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(5):1594-1600,7.基金项目
国家自然科学基金资助项目(62501517,52275003) （62501517,52275003）
中央高校基本科研业务费专项资金资助项目(buctrc202105) （buctrc202105）
