Deep Body-pose Estimation via Synthetic Depth Data: A Case Study

Christopher Pramerdorfer, Martin Kampel

Abstract

Computer Vision research is nowadays largely data-driven due to the prevalence of deep learning. This is one reason why depth data have become less popular, as no datasets exist that are comparable to common color datasets in terms of size and quality. However, depth data have advantages in practical applications that involve people, in which case utilizing cameras raises privacy concerns. We consider one such application, namely 3D human pose estimation for a health care application, to study whether the lack of large depth datasets that represent this problem can be overcome via synthetic data, which aspects must be considered to ensure generalization, and how this compares to alternative approaches for obtaining training data. Furthermore, we compare the pose estimation performance of our method on depth data to that of state-of-the-art methods for color images and show that depth data is a suitable alternative to color images in this regard.

Download


Paper Citation


in Harvard Style

Pramerdorfer C. and Kampel M. (2020). Deep Body-pose Estimation via Synthetic Depth Data: A Case Study.In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, ISBN 978-989-758-402-2, pages 318-325. DOI: 10.5220/0008941303180325


in Bibtex Style

@conference{visapp20,
author={Christopher Pramerdorfer and Martin Kampel},
title={Deep Body-pose Estimation via Synthetic Depth Data: A Case Study},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,},
year={2020},
pages={318-325},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008941303180325},
isbn={978-989-758-402-2},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,
TI - Deep Body-pose Estimation via Synthetic Depth Data: A Case Study
SN - 978-989-758-402-2
AU - Pramerdorfer C.
AU - Kampel M.
PY - 2020
SP - 318
EP - 325
DO - 10.5220/0008941303180325