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
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
相关论文
共 50 条
  • [1] Overview of behavior recognition based on deep learning
    Kai Hu
    Junlan Jin
    Fei Zheng
    Liguo Weng
    Yiwu Ding
    Artificial Intelligence Review, 2023, 56 : 1833 - 1865
  • [2] Speaker recognition based on deep learning: An overview
    Bai, Zhongxin
    Zhang, Xiao-Lei
    NEURAL NETWORKS, 2021, 140 : 65 - 99
  • [3] Group activity recognition based on deep learning: Overview and outlook
    Zhu, Xiao-Lin
    Wang, Dong-Li
    Ouyang, Wan-Li
    Li, Bao-Pu
    Zhou, Yan
    Liu, Jin-Fu
    Kongzhi Lilun Yu Yingyong/Control Theory and Applications, 2024, 41 (12): : 2207 - 2223
  • [4] Student Behavior Recognition in Classroom Based on Deep Learning
    Jia, Qingzheng
    He, Jialiang
    APPLIED SCIENCES-BASEL, 2024, 14 (17):
  • [5] Behavior Recognition of Squid Jigger Based on Deep Learning
    Song, Yifan
    Zhang, Shengmao
    Tang, Fenghua
    Shi, Yongchuang
    Wu, Yumei
    He, Jianwen
    Chen, Yunyun
    Li, Lin
    FISHES, 2023, 8 (10)
  • [6] Substation Behavior Recognition Technology Based on Deep Learning
    Liu Guoming
    Liang Xiaojiao
    Yu Hui
    Lu Zhixing
    Kang Kai
    Li Tengchang
    Liu Bin
    2020 CHINESE AUTOMATION CONGRESS (CAC 2020), 2020, : 6228 - 6233
  • [7] An Overview on Underwater Acoustic Passive Target Recognition Based on Deep Learning
    Zhang Q.
    Da L.
    Wang C.
    Zhang Y.
    Zhuo J.
    Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology, 2023, 45 (11): : 4190 - 4202
  • [8] Overview of currency recognition using deep learning
    Qian Zhang
    Wei Qi Yan
    Mohan Kankanhalli
    Journal of Banking and Financial Technology, 2019, 3 (1): : 59 - 69
  • [9] A study of falling behavior recognition of the elderly based on deep learning
    Xu, Pengfei
    Sulaiman, Nor Anis Asma
    Ding, Yafei
    Zhao, Jiangwei
    Li, Shengpu
    SIGNAL IMAGE AND VIDEO PROCESSING, 2024, 18 (10) : 7383 - 7394
  • [10] Bank abnormal behavior recognition technology based on deep learning
    Du, Yueyun
    Engineering Intelligent Systems, 2021, 29 (04): : 235 - 239