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Authors: Thomas Schnürer 1 ; Stefan Fuchs 2 ; Markus Eisenbach 3 and Horst-Michael Groß 3

Affiliations: 1 Neuroinformatics and Cognitive Robotics Lab, Ilmenau University of Technology, 98684 Ilmenau, Germany, Honda Research Institute Europe GmbH, 63073 Offenbach/Main, Germany ; 2 Honda Research Institute Europe GmbH, 63073 Offenbach/Main, Germany ; 3 Neuroinformatics and Cognitive Robotics Lab, Ilmenau University of Technology, 98684 Ilmenau, Germany

ISBN: 978-989-758-354-4

Keyword(s): Real-time 3D Joint Estimation, Human-Robot-Interaction, Deep Learning.

Abstract: To allow for safe Human-Robot-Interaction in industrial scenarios like manufacturing plants, it is essential to always be aware of the location and pose of humans in the shared workspace. We introduce a real-time 3D pose estimation system using single depth images that is aimed to run on limited hardware, such as a mobile robot. For this, we optimized a CNN-based 2D pose estimation architecture to achieve high frame rates while simultaneously requiring fewer resources. Building upon this architecture, we extended the system for 3D estimation to directly predict Cartesian body joint coordinates. We evaluated our system on a newly created dataset by applying it to a specific industrial workbench scenario. The results show that our system’s performance is competitive to the state of the art at more than five times the speed for single person pose estimation.

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Paper citation in several formats:
Schnürer, T.; Fuchs, S.; Eisenbach, M. and Groß, H. (2019). Real-time 3D Pose Estimation from Single Depth Images.In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, ISBN 978-989-758-354-4, pages 716-724. DOI: 10.5220/0007394707160724

@conference{visapp19,
author={Thomas Schnürer. and Stefan Fuchs. and Markus Eisenbach. and Horst{-}Michael Groß.},
title={Real-time 3D Pose Estimation from Single Depth Images},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,},
year={2019},
pages={716-724},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007394707160724},
isbn={978-989-758-354-4},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,
TI - Real-time 3D Pose Estimation from Single Depth Images
SN - 978-989-758-354-4
AU - Schnürer, T.
AU - Fuchs, S.
AU - Eisenbach, M.
AU - Groß, H.
PY - 2019
SP - 716
EP - 724
DO - 10.5220/0007394707160724

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