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

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

Abstract

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 %.

Keywords

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

Notes

Acknowledgments

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

References

  1. 1.
    Avely RP, Wang Y, Rutherford GS (2004) Length-based vehicle classification using images from uncalibrated video cameras. IEEE intelligent transportation systems conference, pp 737–742Google Scholar
  2. 2.
    Zhang G, Avery R, Wang Y (2007) A video-based vehicle detection and classification system for real-time traffic data collection using uncalibrated video cameras. Transp Res Rec: J Transp Res Board 1993:138–147CrossRefGoogle Scholar
  3. 3.
    Hjort M, Haraldsson M, Jansen JM (2008) Road wear from heavy vehicles. NVF committee vehicles and transportsGoogle Scholar
  4. 4.
    Daubaras A, Zilys M (2012) Vehicle detection based on magneto-resistive magnetic field sensor. Electron Electr Eng 118:27–32Google Scholar
  5. 5.
    Sun Z, Bebis G, Miller R (2004) On-road vehicle detection using optical sensors: a review. In: Proceedings of the 7th international IEEE conference on intelligent transportation systems, pp 585–590Google Scholar
  6. 6.
    Bouwmans T, El Baf F, Vachon B (2008) Background modeling using mixture of Gaussians for foreground detection—a survey. Recent Pat Comput Sci 3:219–237CrossRefGoogle Scholar
  7. 7.
    Stauffer C, Grimson W (1999) Adaptive background mixture models for real-time tracking. In: IEEE computer society conference on computer vision and pattern recognitionGoogle Scholar
  8. 8.
    Barnich O, Van Droogenbroeck M (2011) ViBe: a universal background subtraction algorithm for video sequences. IEEE Trans Image Process: Publ IEEE Signal Process Soc 20:1709–1724Google Scholar
  9. 9.
    Razali MT, Jantan A (2008) Support vector machine for classify dynamic human/vehicle shapes. In: International conference on electronic design, pp 1–6Google Scholar
  10. 10.
    Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE computer society conference on computer vision and pattern recognitionGoogle Scholar
  11. 11.
    Teoh SS, Braunl T (2012) Symmetry-based monocular vehicle detection system. Mach Vis Appl 23:831–842CrossRefGoogle Scholar
  12. 12.
    Teoh SS (2011) Development of a robust monocular-based vehicle detection and tracking system. Ph.D. Thesis, The University of Western AustraliaGoogle Scholar
  13. 13.
    Hsu CW, Lin CJ (2002) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Networks 13:415–425Google Scholar
  14. 14.
    Chang CC, Lin CJ (2013) LIBSVM : a library for support vector machines. ACM Trans Intell Syst Technol 2(3):1–39CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Singapore 2014

Authors and Affiliations

  • Lee Teng Ng
    • 1
  • 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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