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Sensors 2016, 16(12), 1966; doi:10.3390/s16121966

Human Pose Estimation from Monocular Images: A Comprehensive Survey

1
Department of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China
2
Computer Vision Center, University Autònoma de Barcelona, 08193 Catalonia, Spain
3
Laboratory MIA, University of La Rochelle, 17042 La Rochelle CEDEX, France
4
Laboratory L3i, University of La Rochelle, 17042 La Rochelle CEDEX, France
5
School of Computer Science and Technology, Shandong University, Jinan 250100, China
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 4 July 2016 / Revised: 23 September 2016 / Accepted: 2 November 2016 / Published: 25 November 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [2357 KB, uploaded 25 November 2016]   |  

Abstract

Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image. Human pose estimation from monocular images has wide applications (e.g., image indexing). Several surveys on human pose estimation can be found in the literature, but they focus on a certain category; for example, model-based approaches or human motion analysis, etc. As far as we know, an overall review of this problem domain has yet to be provided. Furthermore, recent advancements based on deep learning have brought novel algorithms for this problem. In this paper, a comprehensive survey of human pose estimation from monocular images is carried out including milestone works and recent advancements. Based on one standard pipeline for the solution of computer vision problems, this survey splits the problem into several modules: feature extraction and description, human body models, and modeling methods. Problem modeling methods are approached based on two means of categorization in this survey. One way to categorize includes top-down and bottom-up methods, and another way includes generative and discriminative methods. Considering the fact that one direct application of human pose estimation is to provide initialization for automatic video surveillance, there are additional sections for motion-related methods in all modules: motion features, motion models, and motion-based methods. Finally, the paper also collects 26 publicly available data sets for validation and provides error measurement methods that are frequently used. View Full-Text
Keywords: human pose estimation; human body models; generative methods; discriminative methods; top-down methods; bottom-up methods human pose estimation; human body models; generative methods; discriminative methods; top-down methods; bottom-up methods
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Gong, W.; Zhang, X.; Gonzàlez, J.; Sobral, A.; Bouwmans, T.; Tu, C.; Zahzah, E.-H. Human Pose Estimation from Monocular Images: A Comprehensive Survey. Sensors 2016, 16, 1966.

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