Fast Self-supervised On-line Training for Object Recognition Specifically for Robotic Applications

Markus Schoeler, Simon Christoph Stein, Jeremie Papon, Alexey Abramov, Florentin Woergoetter

Abstract

Today most recognition pipelines are trained at an off-line stage, providing systems with pre-segmented images and predefined objects, or at an on-line stage, which requires a human supervisor to tediously control the learning. Self-Supervised on-line training of recognition pipelines without human intervention is a highly desirable goal, as it allows systems to learn unknown, environment specific objects on-the-fly. We propose a fast and automatic system, which can extract and learn unknown objects with minimal human intervention by employing a two-level pipeline combining the advantages of RGB-D sensors for object extraction and high-resolution cameras for object recognition. Furthermore, we significantly improve recognition results with local features by implementing a novel keypoint orientation scheme, which leads to highly invariant but discriminative object signatures. Using only one image per object for training, our system is able to achieve a recognition rate of 79% for 18 objects, benchmarked on 42 scenes with random poses, scales and occlusion, while only taking 7 seconds for the training. Additionally, we evaluate our orientation scheme on the state-of-the-art 56-object SDU-dataset boosting accuracy for one training view per object by +37% to 78% and peaking at a performance of 98% for 11 training views.

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Paper Citation


in Harvard Style

Schoeler M., Stein S., Papon J., Abramov A. and Woergoetter F. (2014). Fast Self-supervised On-line Training for Object Recognition Specifically for Robotic Applications . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-004-8, pages 94-103. DOI: 10.5220/0004688000940103


in Bibtex Style

@conference{visapp14,
author={Markus Schoeler and Simon Christoph Stein and Jeremie Papon and Alexey Abramov and Florentin Woergoetter},
title={Fast Self-supervised On-line Training for Object Recognition Specifically for Robotic Applications},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={94-103},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004688000940103},
isbn={978-989-758-004-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014)
TI - Fast Self-supervised On-line Training for Object Recognition Specifically for Robotic Applications
SN - 978-989-758-004-8
AU - Schoeler M.
AU - Stein S.
AU - Papon J.
AU - Abramov A.
AU - Woergoetter F.
PY - 2014
SP - 94
EP - 103
DO - 10.5220/0004688000940103