Hard Negative Mining from in-Vehicle Camera Images based on Multiple Observations of Background Patterns

Masashi Hontani, Haruya Kyutoku, David Wong, Daisuke Deguchi, Yasutomo Kawanishi, Ichiro Ide, Hiroshi Murase

2019

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.

Paper Citation

in Harvard Style

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 (VISIGRAPP 2019) - Volume 5: VISAPP; ISBN 978-989-758-354-4, SciTePress, pages 435-442. DOI: 10.5220/0007376804350442

in Bibtex Style

@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 (VISIGRAPP 2019) - Volume 5: VISAPP},
year={2019},
pages={435-442},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007376804350442},
isbn={978-989-758-354-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - 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
PB - SciTePress