Authors:
Christopher Pramerdorfer
1
;
2
and
Martin Kampel
2
Affiliations:
1
Cogvis, Vienna, Austria
;
2
Computer Vision Lab, TU Wien, Vienna, Austria
Keyword(s):
Deep Learning, Synthetic Depth Data, Body-pose Estimation.
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.