@article{M27D9ED88,
title = "Semantic Occlusion Augmentation for Effective Human Pose Estimation",
journal = "KIPS Transactions on Software and Data Engineering",
year = "2022",
issn = "2287-5905",
doi = "https://doi.org/10.3745/KTSDE.2022.11.12.517",
author = "Hyun-Jae Bae/Jin-Pyung Kim/Jee-Hyong Lee",
keywords = "Data Augmentation, Occlusion, Human Pose Estimation, Deep Learning",
abstract = "Human pose estimation is a method of estimating a posture by extracting a human joint key point. When occlusion occurs, the joint 
key point extraction performance is lowered because the human joint is covered. The occlusion phenomenon is largely divided into three 
types of actions: self-contained, covered by other objects, and covered by background. In this paper, we propose an effective posture 
estimation method using a masking phenomenon enhancement technique. Although the posture estimation method has been continuously 
studied, research on the occlusion phenomenon of the posture estimation method is relatively insufficient. To solve this problem, the 
author proposes a data augmentation technique that intentionally masks human joints. The experimental results in this paper show that 
the intentional use of the blocking phenomenon enhancement technique is strong against the blocking phenomenon and the performance 
is increased."
}