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Support vector machines for traffic signs recognition
Dalarna University, School of Technology and Business Studies, Computer Engineering.ORCID iD: 0000-0002-1429-2345
2008 (English)In: IEEE International Joint Conference on Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence)., Hong Kong, 2008, p. 3820-3827Conference paper, Published paper (Refereed) Published
Abstract [en]

In many traffic sign recognition system, one of the main tasks is to classify the shapes of traffic sign. In this paper, we have developed a shape-based classification model by using support vector machines. We focused on recognizing seven categories of traffic sign shapes and five categories of speed limit signs. Two kinds of features, binary image and Zernike moments, were used for representing the data to the SVM for training and test. We compared and analyzed the performances of the SVM recognition model using different feature representations and different kernels and SVM types. Our experimental data sets consisted of 350 traffic sign shapes and 250 speed limit signs. Experimental results have shown excellent results, which have achieved 100% accuracy on sign shapes classification and 99% accuracy on speed limit signs classification. The performance of SVM model highly depends on the choice of model parameters. Two search algorithms, grid search and simulated annealing search have been implemented to improve the performances of our classification model. The SVM model were also shown to be more effective than Fuzzy ARTMAP model.

Place, publisher, year, edition, pages
Hong Kong, 2008. p. 3820-3827
Keywords [en]
SVM, traffic signs, Recognition, classification, Zernike moments
Identifiers
URN: urn:nbn:se:du-3673OAI: oai:dalea.du.se:3673DiVA, id: diva2:521906
Conference
2008 International Joint Conference on Neural Networks (IJCNN 2008) , Hong Kong, 1-8 june, 2008
Available from: 2009-02-01 Created: 2009-02-01 Last updated: 2016-02-12Bibliographically approved

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Fleyeh, Hasan

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf