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Authors: Masashi Hontani 1 ; Haruya Kyutoku 1 ; David Wong 1 ; Daisuke Deguchi 2 ; Yasutomo Kawanishi 1 ; Ichiro Ide 1 and Hiroshi Murase 1

Affiliations: 1 Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya-shi, Aichi, Japan ; 2 Information Strategy Office, Nagoya University, Furo-cho, Chikusa-ku, Nagoya-shi, Aichi, Japan

ISBN: 978-989-758-354-4

Keyword(s): Pedestrian Detection, Hard Negative Mining, Additional Learning.

Abstract: In recent years, the demand for highly accurate pedestrian detectors has increased due to the development of advanced driving support systems. For the training of an accurate pedestrian detector, it is important to collect a large number of training samples. To support this, this paper proposes a “hard negative” mining method to automatically extract background images which tend to be erroneously detected as pedestrians. Negative samples are selected based on the assumption that frequent patterns observed multiple times in the same location are most likely parts of the background scene. As a result of an evaluation using in-vehicle camera images captured along the same route, we confirmed that the proposed method can automatically collect false positive samples accurately. We also confirmed that a highly accurate detector can be constructed using the additional negative samples.

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Paper citation in several formats:
Hontani, M.; Kyutoku, H.; Wong, D.; Deguchi, D.; Kawanishi, Y.; Ide, I. and Murase, H. (2019). Hard Negative Mining from in-Vehicle Camera Images based on Multiple Observations of Background Patterns.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 435-442. DOI: 10.5220/0007376804350442

@conference{visapp19,
author={Masashi Hontani. and Haruya Kyutoku. and David Wong. and Daisuke Deguchi. and Yasutomo Kawanishi. and Ichiro Ide. and Hiroshi Murase.},
title={Hard Negative Mining from in-Vehicle Camera Images based on Multiple Observations of Background Patterns},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,},
year={2019},
pages={435-442},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007376804350442},
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 - Hard Negative Mining from in-Vehicle Camera Images based on Multiple Observations of Background Patterns
SN - 978-989-758-354-4
AU - Hontani, M.
AU - Kyutoku, H.
AU - Wong, D.
AU - Deguchi, D.
AU - Kawanishi, Y.
AU - Ide, I.
AU - Murase, H.
PY - 2019
SP - 435
EP - 442
DO - 10.5220/0007376804350442

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