Vision-Based Human Pose Estimation via Deep Learning: A Survey

Gongjin Lan1; Yu Wu1; Fei Hu2; Qi Hao1

2022

10.1109/THMS.2022.3219242

IEEE Transactions on Human-Machine Systems   Impact Factor & Quartile

2168-2305

2168-2305

Human pose estimation (HPE) has attracted a significant amount of attention from the computer vision community in the past decades. Moreover, HPE has been applied to various domains, such as human-computer interaction, sports analysis, and human tracking via images and videos. Recently, deep learning-based approaches have shown state-of-the-art performance in HPE-based applications. Although deep learning-based approaches have achieved remarkable performance in HPE, a comprehensive review of deep learning-based HPE methods remains lacking in literature. In this article, we provide an up-to-date and in-depth overview of the deep learning approaches in vision-based HPE. We summarize these methods of 2-D and 3-D HPE, and their applications, discuss the challenges and the research trends through bibliometrics, and provide insightful recommendations for future research. This article provides a meaningful overview as introductory material for beginners to deep learning-based HPE, as well as supplementary material for advanced researchers.

Action recognition bibliometric deep learning human performance assessment human pose estimation (HPE)

SCI

English

First

National Natural Science Foundation of China[61773197] ; Shenzhen Fundamental Research Program[JCYJ20200109141622964] ; Intel ICRI-IACV Research Fund[CG#52514373]

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Cybernetics

WOS:000890810300001

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

IEEE

1.Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
2.Department of Electrical and Computer Engineering, University of Alabama, Tuscaloosa, AL, USA

Gongjin Lan,Yu Wu,Fei Hu,et al. Vision-Based Human Pose Estimation via Deep Learning: A Survey[J]. IEEE Transactions on Human-Machine Systems,2022,PP(99):1-16.

Gongjin Lan,Yu Wu,Fei Hu,&Qi Hao.(2022).Vision-Based Human Pose Estimation via Deep Learning: A Survey.IEEE Transactions on Human-Machine Systems,PP(99),1-16.

Gongjin Lan,et al."Vision-Based Human Pose Estimation via Deep Learning: A Survey".IEEE Transactions on Human-Machine Systems PP.99(2022):1-16.