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SVM based traffic sign classification using legender 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: Proceedings of the 3rd Indian International Conference on Artificial Intelligence, IICAI 2007, 2007, 957-968 p.Conference paper, (Refereed)
Abstract [en]

This paper presents a novel approach to recognize 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 color segmented and all objects which represent signs are extracted and normalized to 36×36 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. Copyright © 2007 IICAI.

Place, publisher, year, edition, pages
2007. 957-968 p.
Keyword [en]
Classification, Legendre moments, SVM, Traffic signs
National Category
Computer Engineering
Identifiers
URN: urn:nbn:se:du-18851Scopus ID: 2-s2.0-84872089290OAI: oai:DiVA.org:du-18851DiVA: diva2:843543
Conference
3rd Indian International Conference on Artificial Intelligence, IICAI 2007; Pune; India; 17-19 December 2007
Available from: 2015-07-29 Created: 2015-07-22 Last updated: 2016-02-12Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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