计算机工程与应用2024，Vol.60Issue(20)：1-29,29.DOI:10.3778/j.issn.1002-8331.2404-0143
骨架人体行为识别研究回顾、现状及展望
Skeleton-Based Human Action Recognition:History,Status and Prospects
摘要
Abstract
Human action recognition has significant application prospects in fields such as video surveillance,human-computer interaction,medical care,and sports event analysis.In recent years,with the rapid development of sensor tech-nology and human pose estimation algorithms,skeleton-based human action recognition has gained increasing attention from researchers.Compared to traditional video or image data,skeleton data have the characteristics of being centered on the human subject,highly abstract motion information,and low data dimensions,providing a new perspective for modeling behavior information.This paper focuses on skeleton-based human action recognition and provides a comprehensive sys-tematic review and analysis of relevant work.Firstly,through a literature citation analysis,it systematically summarizes the development trajectory of skeleton-based action recognition.Based on this,the paper reviews traditional recognition methods based on manual features and deep learning-based methods,focusing on the basic principles,improvement strate-gies,and representative works of convolutional neural networks,recurrent neural networks,graph convolutional neural networks,and Transformer methods,and briefly discusses the research status of network model learning algorithms.Sec-ondly,it summarizes three types of publicly available datasets based on motion capture systems,Kinect camera,and RGB images,and discusses their characteristics and applications in detail.Finally,combined with the current research status and thinking analysis at home and abroad,the paper summarizes the key challenges and difficulties of skeleton-based human action recognition,and looks forward to future development directions,aiming to establish a comprehensive domain research perspective for researchers and provide a reference and inspiration for work in related fields.关键词
骨架行为识别/文献计量分析/时空特征表征/深度学习/神经网络Key words
skeleton-based action recognition/bibliometric analysis/spatial-temporal information representation/deep learning/neural network分类
信息技术与安全科学引用本文复制引用
边存灵,吕伟刚,冯伟..骨架人体行为识别研究回顾、现状及展望[J].计算机工程与应用,2024,60(20):1-29,29.基金项目
国家自然科学基金(62277045). （62277045）
