Geometrical, physical and text/symbol analysis based approach of traffic sign detection system

Yangxing Liu, Takeshi Ikenaga, Satoshi Goto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

Traffic sign detection is a valuable part of future driver support system. In this paper, we present a novel framework to accurately detect traffic signs from a single color image by analyzing geometrical, physical andtext/symbol features of traffic signs. First, we utilize an elaborate edge detection algorithm to extract edge map and accurate edge pixel gradient information. Second 2-D geometric primitives (circles, ellipses, rectangles and triangles) are quickly extracted from image edge map. Third the candidate traffic sign regions are selected by analyzing the intrinsic color features, which are invariant to different illumination conditions, of each region circumvented by geometric primitives. Finally a text and symbol detection algorithm is introduced to classify true traffic signs. Experimental results demonstrated the capabilities of our algorithm to detect traffic signs with respect to different size, shape, color and illumination conditions.

Original languageEnglish
Title of host publication2006 IEEE Intelligent Vehicles Symposium, IV 2006
Pages238-243
Number of pages6
Publication statusPublished - 2006 Dec 1
Event2006 IEEE Intelligent Vehicles Symposium, IV 2006 - Meguro-Ku, Tokyo, Japan
Duration: 2006 Jun 132006 Jun 15

Publication series

NameIEEE Intelligent Vehicles Symposium, Proceedings

Conference

Conference2006 IEEE Intelligent Vehicles Symposium, IV 2006
CountryJapan
CityMeguro-Ku, Tokyo
Period06/6/1306/6/15

ASJC Scopus subject areas

  • Modelling and Simulation
  • Automotive Engineering
  • Computer Science Applications

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  • Cite this

    Liu, Y., Ikenaga, T., & Goto, S. (2006). Geometrical, physical and text/symbol analysis based approach of traffic sign detection system. In 2006 IEEE Intelligent Vehicles Symposium, IV 2006 (pp. 238-243). [1689635] (IEEE Intelligent Vehicles Symposium, Proceedings).