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A Comprehensive Survey of Vision-Based Human Action Recognition Methods

Sensors (Basel, Switzerland), 2019-02, Vol.19 (5), p.1005 [Peer Reviewed Journal]

2019 by the authors. 2019 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s19051005 ;PMID: 30818796

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Title:
A Comprehensive Survey of Vision-Based Human Action Recognition Methods
Author: Zhang, Hong-Bo ; Zhang, Yi-Xiang ; Zhong, Bineng ; Lei, Qing ; Yang, Lijie ; Du, Ji-Xiang ; Chen, Duan-Sheng
Subjects: action detection ; action feature ; Algorithms ; human action recognition ; Human Activities ; Humans ; human–object interaction recognition ; Motion ; Pattern Recognition, Automated - methods ; Review ; Skeleton - physiology ; Surveys and Questionnaires ; systematic survey ; Vision, Ocular - physiology ; Visual Perception - physiology
Is Part Of: Sensors (Basel, Switzerland), 2019-02, Vol.19 (5), p.1005
Description: Although widely used in many applications, accurate and efficient human action recognition remains a challenging area of research in the field of computer vision. Most recent surveys have focused on narrow problems such as human action recognition methods using depth data, 3D-skeleton data, still image data, spatiotemporal interest point-based methods, and human walking motion recognition. However, there has been no systematic survey of human action recognition. To this end, we present a thorough review of human action recognition methods and provide a comprehensive overview of recent approaches in human action recognition research, including progress in hand-designed action features in RGB and depth data, current deep learning-based action feature representation methods, advances in human⁻object interaction recognition methods, and the current prominent research topic of action detection methods. Finally, we present several analysis recommendations for researchers. This survey paper provides an essential reference for those interested in further research on human action recognition.
Publisher: Switzerland: MDPI
Language: English
Identifier: ISSN: 1424-8220
EISSN: 1424-8220
DOI: 10.3390/s19051005
PMID: 30818796
Source: GFMER Free Medical Journals
MEDLINE
PubMed Central
ROAD: Directory of Open Access Scholarly Resources
ProQuest Central
DOAJ Directory of Open Access Journals

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A Comprehensive Survey of Vision-Based Human Action Recognition Methods

2019 by the authors. 2019 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s19051005 ;PMID: 30818796

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