Overview of behavior recognition based on deep learning

被引：47
|
作者：
Hu, Kai [1 ,2 ]
Jin, Junlan [1 ,2 ]
Zheng, Fei [2 ,3 ]
Weng, Liguo [1 ,2 ]
Ding, Yiwu [1 ,2 ]
机构：
[1] Nanjing Univ Informat Sci & Technol, Sch Automat, 219 Ningliu Rd, Nanjing 210044, Jiangsu, Peoples R China
[2] Nanjing Univ Informat Sci & Technol, Jiangsu Collaborat Innovat Ctr Atmospher Environm, 219 Ningliu Rd, Nanjing 210044, Jiangsu, Peoples R China
[3] Innovat Dept Ind Internet, China Telecom Ningbo Branch, 96 HeYi Rd, Ningbo 315000, Zhejiang, Peoples R China
来源：
ARTIFICIAL INTELLIGENCE REVIEW | 2023年 / 56卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Behavior recognition; Deep learning; Skeleton data; NETWORK; LSTM;
D O I：
10.1007/s10462-022-10210-8
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Human behavior recognition has always been a hot spot for research in computer vision. With the wide application of behavior recognition in virtual reality and short video in recent years and the rapid development of deep learning algorithms, behavior recognition algorithms based on deep learning have emerged. Compared with traditional methods, behavior recognition algorithms based on deep learning have the advantages of strong robustness and high accuracy. This paper systemizes and introduces behavior recognition algorithms based on deep learning proposed in recent years, then focuses on a series of behavior recognition algorithms based on image and bone data; deeply analyzes their theories and performance, and finally, puts forward further prospects.
引用
收藏
页码：1833 / 1865
页数：33
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