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Authors: Ronny Hänsch ; Stefan Kaiser and Olaf Helwich

Affiliation: Technische Universität Berlin, Germany

Keyword(s): Object Detection, Random Forests, RGBD-data.

Related Ontology Subjects/Areas/Topics: Applications ; Pattern Recognition ; Robotics ; Software Engineering

Abstract: Most methods of object detection with RGBD cameras set hard constraints on their operational area. They only work with specific objects, in specific environments, or rely on time consuming computations. In the context of home robotics, such hard constraints cannot be made. Specifically, an autonomous home robot shall be equipped with an object detection pipeline that runs in near real-time and produces reliable results without restricting object type and environment. For this purpose, a baseline framework that works on RGB data only is extended by suitable depth features that are selected on the basis of a comparative evaluation. The additional depth data is further exploited to reduce the computational cost of the detection algorithm. A final evaluation of the enhanced framework shows significant improvements compared to its original version and state-of-the-art methods in terms of both, detection performance and real-time capability.

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Paper citation in several formats:
Hänsch, R.; Kaiser, S. and Helwich, O. (2017). Near Real-time Object Detection in RGBD Data. In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) - Volume 5: VISAPP; ISBN 978-989-758-226-4; ISSN 2184-4321, SciTePress, pages 179-186. DOI: 10.5220/0006101401790186

@conference{visapp17,
author={Ronny Hänsch. and Stefan Kaiser. and Olaf Helwich.},
title={Near Real-time Object Detection in RGBD Data},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) - Volume 5: VISAPP},
year={2017},
pages={179-186},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006101401790186},
isbn={978-989-758-226-4},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) - Volume 5: VISAPP
TI - Near Real-time Object Detection in RGBD Data
SN - 978-989-758-226-4
IS - 2184-4321
AU - Hänsch, R.
AU - Kaiser, S.
AU - Helwich, O.
PY - 2017
SP - 179
EP - 186
DO - 10.5220/0006101401790186
PB - SciTePress

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