Boosted road sign detection and recognition

Sin Yu Chen*, Jun-Wei Hsieh

*Corresponding author for this work

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

15 Scopus citations

Abstract

This paper presents a boosted system to detect and recognize roads signs from videos. The system first uses the Adaboost algorithm to learn the visual characteristics of road sign. Then, a cascaded structure is then used to detect road signs from videos in real time. After detection, a rectification process is then applied for rectifying different skewed road signs into a normal one. Then, its all embedded texts can be more accurately recognized using their distance maps. On the map, a weighting function is used to balance the importance between a road sign's inner and outer feature so that its embedded characters can be more accurately recognized. Experimental results have proved the superiority of the proposed method in road sign recognition.

Original languageEnglish
Title of host publicationProceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC
Pages3823-3826
Number of pages4
DOIs
StatePublished - 23 Dec 2008
Event7th International Conference on Machine Learning and Cybernetics, ICMLC - Kunming, China
Duration: 12 Jul 200815 Jul 2008

Publication series

NameProceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC
Volume7

Conference

Conference7th International Conference on Machine Learning and Cybernetics, ICMLC
CountryChina
CityKunming
Period12/07/0815/07/08

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  • Cite this

    Chen, S. Y., & Hsieh, J-W. (2008). Boosted road sign detection and recognition. In Proceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC (pp. 3823-3826). [4621071] (Proceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC; Vol. 7). https://doi.org/10.1109/ICMLC.2008.4621071