Vehicle Classification Using Visual Background Extractor and Multi-class Support Vector Machines

  • Lee Teng NgEmail author
  • Shahrel Azmin Suandi
  • Soo Siang Teoh
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 291)


This paper describes a method to classify vehicle type using computer vision technology. In this study, Visual Background Extractor (ViBe) was used to extract the vehicles from the captured videos. The features of the detected vehicles were extracted using Histogram of Oriented Gradient (HOG). Multi-class Support Vector Machine (SVM) was used to recognise four classes of images: motorcycle, car, lorry and background (without vehicles). The results show that the proposed classifier was able to achieve an average accuracy of 92.3 %.


Vehicle classification Visual background extractor Support vector machines Histogram of oriented gradient 



Specially thanks to Chih-Chung Chang and Chih-Jen Lin for sharing LIBSVM, which contributed greatly to this study.


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Copyright information

© Springer Science+Business Media Singapore 2014

Authors and Affiliations

  • Lee Teng Ng
    • 1
    Email author
  • Shahrel Azmin Suandi
    • 1
  • Soo Siang Teoh
    • 1
  1. 1.Intelligent Biometric Group, School of Electrical and Electronic Engineering, USM Engineering CampusUniversity Sains MalaysiaPulau PinangMalaysia

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