Vehicle Type Classification Using Data Mining Techniques

  • Yu Peng
  • Jesse S. Jin
  • Suhuai Luo
  • Min Xu
  • Sherlock Au
  • Zhigang Zhang
  • Yue Cui
Conference paper

DOI: 10.1007/978-1-4614-3501-3_27

Cite this paper as:
Peng Y. et al. (2013) Vehicle Type Classification Using Data Mining Techniques. In: The Era of Interactive Media. Springer, New York, NY

Abstract

In this paper, we proposed a novel and accurate visual-based vehicle type classification system. The system builts up a classifier through applying Support Vector Machine with various features of vehicle image. We made three contributions here: first, we originally incorporated color of license plate in the classification system. Moreover, the vehicle front was measured accurately based on license plate localization and background-subtraction technique. Finally, type probabilities for every vehicle image were derived from eigenvectors rather than deciding vehicle type directly. Instead of calculating eigenvectors from the whole body images of vehicle in existing methods, our eigenvectors are calculated from vehicle front images. These improvements make our system more applicable and accurate. The experiments demonstrated our system performed well with very promising classification rate under different weather or lighting conditions.

Keywords

Vehicle type classificationLicense plate color recognitionVehicle front extractionEigenvectorType possibilitySVM

Copyright information

© Springer Science+Business Media, LLC 2013

Authors and Affiliations

  • Yu Peng
    • 1
  • Jesse S. Jin
    • 1
  • Suhuai Luo
    • 1
  • Min Xu
    • 2
  • Sherlock Au
    • 3
  • Zhigang Zhang
    • 4
  • Yue Cui
    • 1
  1. 1.The School of DCITUniversity of NewcastleNewcastleAsutralia
  2. 2.Faculty of Eng. & ITUniversity of TechnologySydneyAustralia
  3. 3.Global Advanced Vison LtdHangzhouChina
  4. 4.School of Info.Xi’an University of Finance and EconomicsXi’anChina