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Road Sign Detection Using Eigen Color

  • Luo-Wei Tsai
  • Yun-Jung Tseng
  • Jun-Wei Hsieh
  • Kuo-Chin Fan
  • Jiun-Jie Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4843)

Abstract

This paper presents a novel color-based method to detect road signs directly from videos. A road sign usually has specific colors and high contrast to its background. Traditional color-based approaches need to train different color detectors for detecting road signs if their colors are different. This paper presents a novel color model derived from Karhunen-Loeve(KL) transform to detect road sign color pixels from the background. The proposed color transform model is invariant to different perspective effects and occlusions. Furthermore, only one color model is needed to detect various road signs. After transformation into the proposed color space, a RBF (Radial Basis Function) network is trained for finding all possible road sign candidates. Then, a verification process is applied to these candidates according to their edge maps. Due to the filtering effect and discriminative ability of the proposed color model, different road signs can be very efficiently detected from videos. Experiment results have proved that the proposed method is robust, accurate, and powerful in road sign detection.

Keywords

Radial Basis Function Detection Result Color Model Road Sign Color Classification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Luo-Wei Tsai
    • 1
  • Yun-Jung Tseng
    • 1
  • Jun-Wei Hsieh
    • 2
  • Kuo-Chin Fan
    • 1
  • Jiun-Jie Li
    • 1
  1. 1.Department of CSIE, National Central University Jung-Da Rd., Chung-Li 320, Email: echoo@fox1.csie.ncu.edu.twTaiwan
  2. 2.Department of E. E., Yuan Ze University, 135 Yuan-Tung Road, Chung-Li 320Taiwan

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