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.
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