Monocular human pose estimation: A survey of deep learning-based methods

被引:230
|
作者
Chen, Yucheng [1 ]
Tian, Yingli [2 ]
He, Mingyi [1 ]
机构
[1] Northwestern Polytech Univ, Xian 710072, Peoples R China
[2] CUNY, City Coll, New York, NY 10031 USA
基金
美国国家科学基金会;
关键词
Deep learning; Human pose estimation; Survey; HUMAN MOTION ANALYSIS; RECOGNITION; CAPTURE; MODEL; VIDEO;
D O I
10.1016/j.cviu.2019.102897
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Vision-based monocular human pose estimation, as one of the most fundamental and challenging problems in computer vision, aims to obtain posture of the human body from input images or video sequences. The recent developments of deep learning techniques have been brought significant progress and remarkable breakthroughs in the field of human pose estimation. This survey extensively reviews the recent deep learning-based 2D and 3D human pose estimation methods published since 2014. This paper summarizes the challenges, main frameworks, benchmark datasets, evaluation metrics, performance comparison, and discusses some promising future research directions.
引用
收藏
页数:20
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