Human activity recognition: A review of deep learning-based methods

被引:0
|
作者
Dutta, Sanjay Jyoti [1 ]
Boongoen, Tossapon [1 ]
Zwiggelaar, Reyer [1 ]
机构
[1] Aberystwyth Univ, Dept Comp Sci, Aberystwyth, Wales
关键词
computer vision; gesture recognition; surveillance; video surveillance; CONVOLUTIONAL NEURAL-NETWORKS; SPATIOTEMPORAL FEATURES; SKELETON; MODEL;
D O I
10.1049/cvi2.70003
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Human Activity Recognition (HAR) covers methods for automatically identifying human activities from a stream of data. End-users of HAR methods cover a range of sectors, including health, self-care, amusement, safety and monitoring. In this survey, the authors provide a thorough overview of deep learning based and detailed analysis of work that was performed between 2018 and 2023 in a variety of fields related to HAR with a focus on device-free solutions. It also presents the categorisation and taxonomy of the covered publication and an overview of publicly available datasets. To complete this review, the limitations of existing approaches and potential future research directions are discussed.
引用
收藏
页数:27
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