空军工程大学学报2026，Vol.27Issue(2)：82-89,8.DOI:10.3969/j.issn.2097-1915.2026.02.010
基于注意力机制的GCN-Bi-LSTM动作识别方法
An Action Recognition Method Based on Attention Mechanism Using GCN-Bi-LSTM Network
段荣 1李媛 1刘琦2
作者信息
- 1. 空军工程大学信息与导航学院,西安,710077
- 2. 武警陕西总队,西安,710054
- 折叠
摘要
Abstract
In response to the challenges posed by existing action recognition methods that rely on skeleton information,particularly their low utilization of skeletal sequence data and difficulties in accurate recogni-tion,this paper presents a novel action recognition approach utilizing a graph convolutional bidirectional long short-term memory(GCN-Bi-LSTM)network enhanced by an attention mechanism.Firstly,a non-physical dependency relationship is constructed by leveraging the relative distances between skeleton nodes to enrich skeletal features.Secondly,a spatio-temporal graph convolutional network is employed to extract features from each video frame,thereby obtaining more sophisticated semantic representations.Finally,these frame-specific features are fed into the bidirectional LSTM network augmented with an attention mechanism to capture global temporal characteristics and then action discrimination capabilities can be en-hanced effectively.Experimental results show that the proposed method can significantly improve recogni-tion accuracy and has great potential for application in tactical action analysis and training scenarios.关键词
动作识别/全局注意力/骨骼关节/图卷积神经网络/双向长短时记忆Key words
action recognition/global attention/skeletal joints/graph convolutional neural network/bi-di-rectional long short-term memory分类
信息技术与安全科学引用本文复制引用
段荣,李媛,刘琦..基于注意力机制的GCN-Bi-LSTM动作识别方法[J].空军工程大学学报,2026,27(2):82-89,8.
