基于人体骨架的动作识别:综述与展望  

Action Recognition Based on Human Skeleton:Review and Prospect

作　　者：孟祥璞 李硕[3] 苑明哲 王文洪[1,2] 张志佳 宋纯贺[3] 曹飞道 MENG Xiangpu;LI Shuo;YUAN Mingzhe;WANG Wenhong;ZHANG Zhijia;SONG Chunhe;CAO Feidao(College of Artificial Intelligence,Shenyang University of Technology,Shenyang 110870,China;Guangzhou Institute of Industrial Intelligence,Guangzhou 511458,China;Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 110016,China)

机构地区：[1]沈阳工业大学人工智能学院,辽宁沈阳110870 [2]广州工业智能研究院,广东广州511458 [3]中国科学院沈阳自动化研究所,辽宁沈阳110016

出　　处：《信息与控制》2025年第1期1-27,共27页Information and Control

基　　金：国家自然科学基金面上项目(62273337);中国科学院科技服务网络计划(STS)-东莞专项(20211600200072)。

摘　　要：人体动作识别在多场景、多任务下具有多样的研究价值,在智能安防、自动驾驶、人机交互等领域存在广泛的应用前景。基于人体骨架的动作识别已进行了广泛研究,但还没有文献系统地整理其发展历程,并剖析更深层次的内在逻辑。本文整理了基于人体骨架的动作识别的主要发展历程,按照技术方法将其整理归纳为循环神经网络、卷积神经网络、图卷积神经网络、Transformer四大技术路线,并梳理了其不同的发展脉络,分析了两大关键技术点:空间建模与时间建模,指出了构建丰富表征输入信息的方法论;同时讨论了人体骨架模态在多模态融合中对动作识别的重要意义;最后,对人体骨架动作识别技术方法和实际应用进行了展望。Human action recognition holds diverse research value across various scenarios and tasks,with promising applications in intelligent security,autonomous driving,and human-computer interaction.Although extensive research has been conducted on action recognition using human skeletal data,a systematic review of its development trajectory and underlying logic remain lacking.We review the major milestones in human skeletal action recognition,categorizing them into four key technological approaches:recurrent neural networks,convolutional neural networks,graph convolutional networks,and transformers.The developmental contexts of these methods are outlined,with an analysis of two key technological aspects:spatial modeling and temporal modeling.Strategies for constructing rich input representations are also highlighted.Additionally,the significance of skeletal modalities in multimodal integration for action recognition is discussed.Finally,we discusse future directions for techniques and applications in human skeletal action recognition.

关 键 词：动作识别 人体骨架 深度学习 图卷积神经网络 TRANSFORMER 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]