基于骨骼图神经网络的人体行为识别综述  

Review of Human Action Recognition Based on Skeletal Graph Neural Networks

作　　者：蒋悦晗 陈俊杰[1,2] 李洪均 JIANG Yuehan;CHEN Junjie;LI Hongjun(School of Information Science and Technology,Nantong University,Nantong,Jiangsu 226019,China;Nantong Research Institute for Advanced Communication Technologies,Nantong,Jiangsu 226019,China)

机构地区：[1]南通大学信息科学技术学院,江苏南通226019 [2]南通先进通信技术研究院有限公司,江苏南通226019

出　　处：《计算机工程与应用》2025年第3期34-47,共14页Computer Engineering and Applications

基　　金：国家自然科学基金(62371261);南通市科技计划基金(JC2023076);江苏省研究生科研与实践创新计划项目(KYCX24_3643)。

摘　　要：基于骨骼图神经网络的人体行为识别凭借其简洁性和鲁棒性引起了人们的广泛关注,图数据对于处理人体骨骼信息有天然的优势,逐渐成为行为识别领域的研究热点。从行为识别这个宽泛的基本概念入手,进一步引入用骨骼图神经网络进行的人体行为识别任务,分别从4个方面对近些年基于骨骼图神经网络的人体行为识别的研究成果进行了归纳总结;介绍了图结构构造拓扑图的不同方法分类、行为识别模型中的常用机制、目前常用的数据集及评价指标与目前主流方法的比较。最后,针对目前的研究状况对基于骨骼图神经网络的人体行为识别存在的问题进行详细的阐述,并立足于研究现状对该领域的未来发展进行了展望。Human action recognition based on skeletal graph neural network has attracted much attention by virtue of its simplicity and robustness,and graph data have a natural advantage in processing human skeletal information,which has gradually become a research hotspot in the field of action recognition.Starting from the broad basic concept of action recognition,this paper further introduces the task of human action recognition using skeletal graph neural networks,and summarizes the research results on human action recognition using skeletal graph neural networks in recent years from four aspects.The paper introduces different methods for constructing topological graphs with graph structures,the common mechanisms used in action recognition models,the comparison of commonly used datasets and evaluation indicators with mainstream methods.Finally,the problems of human action recognition based on skeletal graph neural networks are elaborated in detail with respect to the current state of research,and an outlook on the future development of the field is given based on the current state of research.

关 键 词：行为识别 深度学习 图神经网络 注意力机制 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]