Deep learning‐based action recognition with 3D skeleton: A survey
作者机构：South China Normal UniversityGuangzhouChina
出 版 物：《CAAI Transactions on Intelligence Technology》 (智能技术学报（英文）)
年 卷 期：2021年第6卷第1期
页 面：80-92页
核心收录：
学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：National Natural Science Foundation of China Grant/Award Numbers:61877020 62077015
摘 要：Action recognition based on 3D skeleton data has attracted much attention due to its wide application,and it is one of the most popular research topics in computer *** 3D skeleton data is an effective representation of motion dynamics and is not easily affected by light,scene variation,*** research on action recognition has mainly focused on video or RGB data *** recent years,the advantages of combining skeleton data and deep learning have been gradually demonstrated,many impressive methods have been proposed,especially GCN‐based *** this survey,we first introduce the development process of 3D skeleton‐data action recognition and the classification of graph convolutional network,then introduce the commonly used NTU RGB+D and NTU RGB+D 120 ***,a detailed review of existing variants of three mainstream technologies is provided based on deep learning and their perfor-mance was compared from three *** the best of our knowledge,this is the first research to integrate the research of GCN‐based method and its various evolutionary *** investigation of existing variants of research in action‐recognition task from different perspectives is made,a generic framework is described,state‐of‐theart practices are summarized,and the emerging trends of this topic are explored.
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