USE OF ADAPTIVE BOOSTING IN FEATURE SELECTION FOR VEHICLE MAKE & MODEL RECOGNITION

I. Zafar, B. S. Acar, E. A. Edirisinghe

2009

Abstract

Vehicle Make and Model Recognition (Vehicle MMR) systems that are capable of improving the trustworthiness of automatic number plate recognitions systems have received attention of the research community in the recent past. Out of a number of algorithms that have been proposed in literature the use of Scale Invariant Feature Transforms (SIFT) in particular have been able to demonstrate the ability to perform vehicle MMR, invariant to scale, rotation, translation, which forms typical challenges of the application domain. In this paper we propose a novel approach to SIFT based vehicle MMR in which SIFT features are initially investigated for their relevance in representing the uniqueness of the make and model of a given vehicle class based on Adaptive Boosting. We provide experimental results to show that the proposed selection of SIFT features significantly reduces the computational cost associated with classification at negligible loss of the system accuracy. We further prove that the use of more appropriate feature matching algorithms enable significant gains in the accuracy of the algorithm. Experimental results prove that a 91% accuracy rate has been achieved on a publically available database of car frontal views.

References

  1. Petrovic, V., Cootes, T. 2004.Analysis of features for rigid structure vehicle type recognition. In: BMVC, September7-9, Kingston.
  2. Dlagnekov,L. 2005. Video-based car surveillance: license plate make and model recognition. Masters thesis, California University.
  3. Zafar,I.,Edirisinghe,E.A,Acar,S. 2008.Vehicle make and model identification in contourlet domain using localised directional feature maps.In:Proceeding Visualization, imaging, and Image Processing, Palma de Mallorca, Spain, Villanueva, J.J.(eds.), ISBN 978- 0-88986-760-4, September 2008.
  4. Lowe, D.G. 2004. Distinctive Image Features from ScaleInvariant Keypoints. International Journal of Computer Vision, 60(2) pp. 91-110.
  5. Zafar, I., Edirisinghe, E.A, Acar, B. S. 2007. Vehicle Make & Model Identification using Scale Invariant Transforms .In: Proceeding (583) Visualization, Imaging, and Image Processing, Palma de Mallorca, Spain, , Villanueva, J.J.(eds), ISBN 978-0-88986-692- 8, August 2007.
  6. Negri, P., Clady, X., Milgram, M., Poulenard, R. 2006.An Oriented-Contour Point Based Voting Algorithm for Vehicle Type Classification. In: Proceedings of the 18th International Conference on Pattern Recognition pp. 574-577.
  7. Cheung,S., and Chu,A. 2008. Make and Model Recognition of Cars. CSE 190A Projects in Vision and Learning, Final Report. California University Fischler, M.A., and Bolles, R.C. 1981.Random sample consensus: A paradigm for model fitting with application to image analysis and automated cartography. Communications of the ACM, 24(6) pp.381-395.
  8. Freund, Y., and Schapire, R. 1999. A short introduction to boosting. Journal of Japanese Society for Artificial Intelligence 14(5) September pp. 771-780.
  9. Freund, Y., and Schapire, R. 1997. A decision-theoretic generalization of on-line learning and an application boosting. Journal of Computer and System Sciences,55(1) August,pp.119-139.
  10. Caprile,B. 2002.Multiple classifier systems, Springer Berlin/Heidelberg.pp. 663-668.
  11. Information Entropy.2008. [Internet].Available from <http://en.wikipedia.org/wiki/Information_entropy > [Accessed September 11 2008].
Download


Paper Citation


in Harvard Style

Zafar I., Acar B. and Edirisinghe E. (2009). USE OF ADAPTIVE BOOSTING IN FEATURE SELECTION FOR VEHICLE MAKE & MODEL RECOGNITION . In Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2009) ISBN 978-989-8111-69-2, pages 142-147. DOI: 10.5220/0001774501420147


in Bibtex Style

@conference{visapp09,
author={I. Zafar and B. S. Acar and E. A. Edirisinghe},
title={USE OF ADAPTIVE BOOSTING IN FEATURE SELECTION FOR VEHICLE MAKE & MODEL RECOGNITION },
booktitle={Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2009)},
year={2009},
pages={142-147},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001774501420147},
isbn={978-989-8111-69-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2009)
TI - USE OF ADAPTIVE BOOSTING IN FEATURE SELECTION FOR VEHICLE MAKE & MODEL RECOGNITION
SN - 978-989-8111-69-2
AU - Zafar I.
AU - Acar B.
AU - Edirisinghe E.
PY - 2009
SP - 142
EP - 147
DO - 10.5220/0001774501420147