华侨大学学报（自然科学版）2026，Vol.47Issue(1)：83-92,10.DOI:10.11830/ISSN.1000-5013.202510014
基于动作特异图卷积与注意力机制的行为识别方法
Action Recognition Method Based on Action-Specific Graph Convolution and Attention Mechanism
摘要
Abstract
To address the challenges of human action recognition on resource-constrained edge devices,en-hance model robustness,and alleviate confusion among similar actions,a behavior recognition network based on action-specific graph convolution and attention mechanism network(ASGCA-Net)is proposed.In the tem-poral dimension,a multi-scale temporal convolutional attention module is designed to simultaneously capture short-term local motion patterns and long-term global dependencies,thereby strengthening the temporal mod-eling capability of action sequences.The attention mechanism is further employed to learn the weight impor-tance of each channe.In the spatial dimension,implicit edges are introduced into the fixed topology to supple-ment joint dependencies across physical connections,and a gating mechanism is used to adaptively adjust the weights of structural and implicit edges,enabling action-specific feature modeling for different action types.Finally,ASGCA-Net is evaluated on the NTU RGB+D and the NTU RGB+D 120 datasets.The results show that compared with the baseline network 2s-AGCN,ASGCA-Net achieves substantial accuracy improve-ments on both datasets.关键词
人体行为识别/图卷积/时间卷积/注意力机制Key words
human action recognition/graph convolution/temporal convolution/attention mechanism分类
信息技术与安全科学引用本文复制引用
吴增群,李国刚,陈金凯,陈俊良..基于动作特异图卷积与注意力机制的行为识别方法[J].华侨大学学报（自然科学版）,2026,47(1):83-92,10.基金项目
国家自然科学基金资助项目(61370007) （61370007）
