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Human pose estimation has received significant attention recently due to its various applications in the real world. As the performance of the state-of-the-art human pose estimation methods can be improved by deep learning, this paper presents a comprehensive survey of deep learning based human pose estimation methods and analyzes the methodologies employed. We summarize and discuss recent works with a methodology-based taxonomy. Single-person and multi-person pipelines are first reviewed separately. Then, the deep learning techniques applied in these pipelines are compared and analyzed. The datasets and metrics used in this task are also discussed and compared. The aim of this survey is to make every step in the estimation pipelines interpretable and to provide readers a readily comprehensible explanation. Moreover, the unsolved problems and challenges for future research are discussed.


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Deep Learning Based 2D Human Pose Estimation: A Survey

Show Author's information Qi DangJianqin Yin*( )Bin WangWenqing Zheng
Automation School, Beijing University of Posts and Telecommunications, Beijing 100876, China.
State Key Lab. of Intelligent Technology and Systems, Tsinghua University, Beijing 100084, China.
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Abstract

Human pose estimation has received significant attention recently due to its various applications in the real world. As the performance of the state-of-the-art human pose estimation methods can be improved by deep learning, this paper presents a comprehensive survey of deep learning based human pose estimation methods and analyzes the methodologies employed. We summarize and discuss recent works with a methodology-based taxonomy. Single-person and multi-person pipelines are first reviewed separately. Then, the deep learning techniques applied in these pipelines are compared and analyzed. The datasets and metrics used in this task are also discussed and compared. The aim of this survey is to make every step in the estimation pipelines interpretable and to provide readers a readily comprehensible explanation. Moreover, the unsolved problems and challenges for future research are discussed.

Keywords:

human pose estimation, deep learning, computer vision
Received: 05 March 2018 Revised: 30 April 2018 Accepted: 03 May 2018 Published: 05 December 2019 Issue date: December 2019
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Publication history

Received: 05 March 2018
Revised: 30 April 2018
Accepted: 03 May 2018
Published: 05 December 2019
Issue date: December 2019

Copyright

© The author(s) 2019

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 61673192, 61573219, and 61472163), the Fund for Outstanding Youth of Shandong Provincial High School (No. ZR2016JL023), the National High-Tech Research and Development Plan (No. 2015AA042306), and the National Social Science Fund Project (No. 13CTQ010).

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