控制理论与应用2024，Vol.41Issue(12)：2207-2223,17.DOI:10.7641/CTA.2023.20375
基于深度学习的群体行为识别:综述与展望
Group activity recognition based on deep learning:Overview and outlook
摘要
Abstract
Group activity recognition has attracted much attention in the computer vision community,and it is widely applied in intelligent monitoring systems and sports video analysis.This paper provides a comprehensive review of the group activity recognition methods based on deep learning over the past seven years,which will help to promote the development of group activity recognition.First,the definition,the general recognition process,and the main challenges of group activity are introduced;Secondly,we classify the group activity recognition methods in modeling and internal mechanism,subdivide them,and further discuss the advantages and disadvantages of these methods;Thirdly,we present the common datasets of group activity recognition,the relevant open-source code libraries,and the evaluation index;Finally,we analyze the future research directions in group activity recognition.关键词
群体行为识别/深度学习/层级时序建模/交互关系推理/TransformerKey words
group activity recognition/deep learning/hierarchical temporal modeling/interaction relationship reason-ing/Transformer引用本文复制引用
朱晓林,王冬丽,欧阳万里,李抱朴,周彦,刘金富..基于深度学习的群体行为识别:综述与展望[J].控制理论与应用,2024,41(12):2207-2223,17.基金项目
国家重点研发计划项目(2020YFA0713503),国家自然科学基金项目(61773330),国家航空科学基金项目(20200020114004),湖南省科技创新计划项目(2020GK2036),湖南省自然科学基金项目(2023JJ30598),湖南省研究生科研创新项目(CX20220652)资助.Supported by the National Key Research and Development Project of China(2020YFA0713503),the National Natural Science Foundation of China(61773330),the Aeronautical Science Foundation of China(20200020114004),the Science and Technology Innovation Program of Hunan Province(2020GK2036),the Natural Science Foundation of Hunan Province(2023JJ30598)and the Postgraduate Research Innovation Project of Hunan Pro-vince(CX20220652). （2020YFA0713503）
