Road speed sign recognition using edge-voting principle and learning vector quantization network

Hsin Han Chiang*, Yen Lin Chen, Wen Qing Wang, Tsu Tian Lee

*Corresponding author for this work

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

10 Citations (Scopus)


This paper presents an automatic speed sign detection and recognition for providing the visual driving-assistance of speed limits awareness. To reduce the influence of digital noise caused by lighting condition and pollution, a segmentation based on pan-red color information is applied to extract the shape of speed sign. Based on the edge-phase information of a circle shape, a novel edge-voting principle is proposed for fast detecting the speed sign candidate from road scenes. The recognition of the content of speed sign is achieved through a modified learning vector quantization (LVQ) network which also verifies each candidate to eliminate non-target blobs. Results show a high success rate and a low amount of false positives in both detection and recognition strategy under a wide variety of visual conditions.

Original languageEnglish
Title of host publicationICS 2010 - International Computer Symposium
Number of pages6
Publication statusPublished - 2010
Externally publishedYes
Event2010 International Computer Symposium, ICS 2010 - Tainan, Taiwan
Duration: 2010 Dec 162010 Dec 18

Publication series

NameICS 2010 - International Computer Symposium


Other2010 International Computer Symposium, ICS 2010


  • Color segmentation
  • Detection
  • Edge-voting
  • Learning Vector Quantization (LVQ) network
  • Recognition
  • Speed sign

ASJC Scopus subject areas

  • Computer Science(all)


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