Monocular Human Pose Estimation: A Survey of Deep Learning-based Methods

PubDate: Jun 2020

Teams: Northwestern Polytechnical University, City University of New York

Writers: Yucheng Chen, Yingli Tian, Mingyi He

PDF: Monocular Human Pose Estimation: A Survey of Deep Learning-based Methods

Abstract

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.