Open AccessA survey on Pose Estimation using Deep Convolutional Neural Networks
Author(s) -
Manisha Patel,
Nilesh Kalani
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1042/1/012008
Subject(s) - pose , artificial intelligence , computer science , convolutional neural network , estimation , computer vision , 3d pose estimation , articulated body pose estimation , augmented reality , deep learning , animation , pattern recognition (psychology) , computer graphics (images) , engineering , systems engineering
Human pose estimation is a technique, which identifies the human body’s landmarks in images and videos. Human pose estimation can be divided into single person pose estimation and multi-person pose estimation, also an estimated human poses in crowded places as well as in videos. Depends upon the application such as activity recognition, Animation, Sports, Augmented reality, etc., Pose estimation output can be in 2D or 3D coordinate format. 3D pose estimation is estimated considering joint angles in 2D. Some challenges like small and barely visible joints, strong articulations, occlusions, clothing, and lighting changes increase difficulty in estimating pose. Remarkable progress has gained in the field of human pose estimation using Deep learning-based CNN models. In this paper, we compare and summarized various deep learning models for pose estimation of a single person and multi-person.