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

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

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

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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