Tsinghua Science and Technology  2019, Vol. 24 Issue (06): 663-676    doi: 10.26599/TST.2018.9010100
 SPECIAL SECTION ON COGNITIVE SYSTEMS AND COMPUTATION
Deep Learning Based 2D Human Pose Estimation: A Survey
Qi Dang, Jianqin Yin*, Bin Wang, Wenqing Zheng
∙ Qi Dang and Jianqin Yin are with Automation School, Beijing University of Posts and Telecommunications, Beijing 100876, China. E-mail: dangqi213@163.com.
∙ Qi Dang and Bin Wang are with State Key Lab. of Intelligent Technology and Systems, Tsinghua University, Beijing 100084, China. E-mail: wangbinth@tsinghua.edu.cn.
∙ Wenqing Zheng is with School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China. E-mail: zhengwenqing@bupt.edu.cn.

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.

Received: 05 March 2018      Published: 20 June 2019
Corresponding Authors: Jianqin Yin