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Authors: I. Zafar ; B. S. Acar and E. A. Edirisinghe

Affiliation: Department of Computer Science, Loughborough University, United Kingdom

Keyword(s): Vehicle make & model recognition, Scale invariant feature transform, AdaBoost, Classification, Feature matching.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computer Vision, Visualization and Computer Graphics ; Data Manipulation ; Feature Extraction ; Features Extraction ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Image and Video Analysis ; Informatics in Control, Automation and Robotics ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Segmentation and Grouping ; Sensor Networks ; Signal Processing, Sensors, Systems Modeling and Control ; Soft Computing

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 th e 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. (More)

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Paper citation in several formats:
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 (VISIGRAPP 2009) - Volume 2: VISAPP; ISBN 978-989-8111-69-2; ISSN 2184-4321, SciTePress, pages 142-147. DOI: 10.5220/0001774501420147

@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 (VISIGRAPP 2009) - Volume 2: VISAPP},
year={2009},
pages={142-147},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001774501420147},
isbn={978-989-8111-69-2},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the Fourth International Conference on Computer Vision Theory and Applications (VISIGRAPP 2009) - Volume 2: VISAPP
TI - USE OF ADAPTIVE BOOSTING IN FEATURE SELECTION FOR VEHICLE MAKE & MODEL RECOGNITION
SN - 978-989-8111-69-2
IS - 2184-4321
AU - Zafar, I.
AU - Acar, B.
AU - Edirisinghe, E.
PY - 2009
SP - 142
EP - 147
DO - 10.5220/0001774501420147
PB - SciTePress