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SVM Based Traffic Sign Classification Using Legendre Moments
Dalarna University, School of Technology and Business Studies, Computer Engineering.ORCID iD: 0000-0002-1429-2345
Dalarna University, School of Technology and Business Studies, Computer Engineering.
2007 (English)In: Third Indian International Conference on Artificial Intelligence, Pune, India, 2007Conference paper, Published paper (Refereed) Published
Abstract [en]

This paper presents a novel approach to recognise traffic signs using Support Vector Machines (SVMs) and Legendre Moments. Images of traffic signs are collected by a digital camera mounted in a vehicle. They are colour segmented and all objects which represent signs are extracted and normalised to 36x36 pixels images. Legendre moments of sign borders and speed-limit signs of 350 and 250 images are computed and the SVM classifier is trained with theses features. Two stages of SVM are trained; the first stage determines the class of the sign from the shape of its border and the second one determines the pictogram of the sign. Training and testing of both SVM classifiers are done offline by using still images. In the online mode, the system loads the SVM training model and performs recognition.

Place, publisher, year, edition, pages
Pune, India, 2007.
Keyword [en]
Traffic signs, Legendre moments, SVM, Classification.
Identifiers
URN: urn:nbn:se:du-3038OAI: oai:dalea.du.se:3038DiVA, id: diva2:521787
Conference
Third Indian International Conference on Artificial Intelligence, Pune, India, 17-19 December 2007, 2007
Available from: 2008-01-03 Created: 2008-01-03 Last updated: 2016-02-12Bibliographically approved

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Fleyeh, Hasan

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CiteExportLink to record
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