LOCAL KERNEL COLOR HISTOGRAMS FOR BACKGROUND SUBTRACTION

Philippe Noriega, Benedicte Bascle, Olivier Bernier

Abstract

In addition to being invariant to image rotation and translation, histograms have the advantage of being easy to compute. These advantages make histograms very popular in computer vision. However, without data quantization to reduce size, histograms are generally not suitable for realtime applications. Moreover, they are sensitive to quantization errors and lack any spatial information. This paper presents a way to keep the advantages of histograms avoiding their inherent drawbacks using local kernel histograms. This approach is tested for background subtraction using indoor and outdoor sequences.

References

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Paper Citation


in Harvard Style

Noriega P., Bascle B. and Bernier O. (2006). LOCAL KERNEL COLOR HISTOGRAMS FOR BACKGROUND SUBTRACTION . In Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, ISBN 972-8865-40-6, pages 213-219. DOI: 10.5220/0001363302130219


in Bibtex Style

@conference{visapp06,
author={Philippe Noriega and Benedicte Bascle and Olivier Bernier},
title={LOCAL KERNEL COLOR HISTOGRAMS FOR BACKGROUND SUBTRACTION},
booktitle={Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP,},
year={2006},
pages={213-219},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001363302130219},
isbn={972-8865-40-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP,
TI - LOCAL KERNEL COLOR HISTOGRAMS FOR BACKGROUND SUBTRACTION
SN - 972-8865-40-6
AU - Noriega P.
AU - Bascle B.
AU - Bernier O.
PY - 2006
SP - 213
EP - 219
DO - 10.5220/0001363302130219