Improved color barycenter model and its separation for road sign detection

Qieshi Zhang, Sei Ichiro Kamata

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)2839-2849
Number of pages11
JournalIEICE Transactions on Information and Systems
Issue number12
Publication statusPublished - 2013 Dec


  • Color barycenter model (CBM)
  • Color triangle
  • Constrained clustering
  • Driver assistance system (DAS)
  • Road sign (RS) detection

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence


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