Improved Color Barycenter Model and Its Separation for Road Sign Detection

Qieshi ZHANG; Sei-ichiro KAMATA

doi:10.1587/transinf.E96.D.2839

Improved Color Barycenter Model and Its Separation for Road Sign Detection

Qieshi ZHANG, Sei-ichiro KAMATA

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This paper proposes an improved color barycenter model (CBM) and its separation for automatic road sign (RS) detection. The previous version of CBM can find out the colors of RS, but the accuracy is not high enough for separating the magenta and blue regions and the influence of number with the same color are not considered. In this paper, the improved CBM expands the barycenter distribution to cylindrical coordinate system (CCS) and takes the number of colors at each position into account for clustering. Under this distribution, the color information can be represented more clearly for analyzing. Then aim to the characteristic of barycenter distribution in CBM (CBM-BD), a constrained clustering method is presented to cluster the CBM-BD in CCS. Although the proposed clustering method looks like conventional K-means in some part, it can solve some limitations of K-means in our research. The experimental results show that the proposed method is able to detect RS with high robustness.

Publication: IEICE TRANSACTIONS on Information Vol.E96-D No.12 pp.2839-2849

Publication Date: 2013/12/01

Publicized

Online ISSN: 1745-1361

DOI: 10.1587/transinf.E96.D.2839

Type of Manuscript: PAPER

Category: Image Recognition, Computer Vision

Authors

Qieshi ZHANG
Waseda University
Sei-ichiro KAMATA
Waseda University

Keyword

road sign (RS) detection, driver assistance system (DAS), color triangle, color barycenter model (CBM), constrained clustering

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Qieshi ZHANG, Sei-ichiro KAMATA, "Improved Color Barycenter Model and Its Separation for Road Sign Detection" in IEICE TRANSACTIONS on Information, vol. E96-D, no. 12, pp. 2839-2849, December 2013, doi: 10.1587/transinf.E96.D.2839.
Abstract: This paper proposes an improved color barycenter model (CBM) and its separation for automatic road sign (RS) detection. The previous version of CBM can find out the colors of RS, but the accuracy is not high enough for separating the magenta and blue regions and the influence of number with the same color are not considered. In this paper, the improved CBM expands the barycenter distribution to cylindrical coordinate system (CCS) and takes the number of colors at each position into account for clustering. Under this distribution, the color information can be represented more clearly for analyzing. Then aim to the characteristic of barycenter distribution in CBM (CBM-BD), a constrained clustering method is presented to cluster the CBM-BD in CCS. Although the proposed clustering method looks like conventional K-means in some part, it can solve some limitations of K-means in our research. The experimental results show that the proposed method is able to detect RS with high robustness.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E96.D.2839/_p

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@ARTICLE{e96-d_12_2839,
author={Qieshi ZHANG, Sei-ichiro KAMATA, },
journal={IEICE TRANSACTIONS on Information},
title={Improved Color Barycenter Model and Its Separation for Road Sign Detection},
year={2013},
volume={E96-D},
number={12},
pages={2839-2849},
abstract={This paper proposes an improved color barycenter model (CBM) and its separation for automatic road sign (RS) detection. The previous version of CBM can find out the colors of RS, but the accuracy is not high enough for separating the magenta and blue regions and the influence of number with the same color are not considered. In this paper, the improved CBM expands the barycenter distribution to cylindrical coordinate system (CCS) and takes the number of colors at each position into account for clustering. Under this distribution, the color information can be represented more clearly for analyzing. Then aim to the characteristic of barycenter distribution in CBM (CBM-BD), a constrained clustering method is presented to cluster the CBM-BD in CCS. Although the proposed clustering method looks like conventional K-means in some part, it can solve some limitations of K-means in our research. The experimental results show that the proposed method is able to detect RS with high robustness.},
keywords={},
doi={10.1587/transinf.E96.D.2839},
ISSN={1745-1361},
month={December},}

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TY - JOUR
TI - Improved Color Barycenter Model and Its Separation for Road Sign Detection
T2 - IEICE TRANSACTIONS on Information
SP - 2839
EP - 2849
AU - Qieshi ZHANG
AU - Sei-ichiro KAMATA
PY - 2013
DO - 10.1587/transinf.E96.D.2839
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E96-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2013
AB - This paper proposes an improved color barycenter model (CBM) and its separation for automatic road sign (RS) detection. The previous version of CBM can find out the colors of RS, but the accuracy is not high enough for separating the magenta and blue regions and the influence of number with the same color are not considered. In this paper, the improved CBM expands the barycenter distribution to cylindrical coordinate system (CCS) and takes the number of colors at each position into account for clustering. Under this distribution, the color information can be represented more clearly for analyzing. Then aim to the characteristic of barycenter distribution in CBM (CBM-BD), a constrained clustering method is presented to cluster the CBM-BD in CCS. Although the proposed clustering method looks like conventional K-means in some part, it can solve some limitations of K-means in our research. The experimental results show that the proposed method is able to detect RS with high robustness.
ER -