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Proceedings Paper

Two-dimensional statistical linear discriminant analysis for real-time robust vehicle-type recognition
Author(s): I. Zafar; E. A. Edirisinghe; S. Acar; H. E. Bez
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Paper Abstract

Automatic vehicle Make and Model Recognition (MMR) systems provide useful performance enhancements to vehicle recognitions systems that are solely based on Automatic License Plate Recognition (ALPR) systems. Several car MMR systems have been proposed in literature. However these approaches are based on feature detection algorithms that can perform sub-optimally under adverse lighting and/or occlusion conditions. In this paper we propose a real time, appearance based, car MMR approach using Two Dimensional Linear Discriminant Analysis that is capable of addressing this limitation. We provide experimental results to analyse the proposed algorithm's robustness under varying illumination and occlusions conditions. We have shown that the best performance with the proposed 2D-LDA based car MMR approach is obtained when the eigenvectors of lower significance are ignored. For the given database of 200 car images of 25 different make-model classifications, a best accuracy of 91% was obtained with the 2D-LDA approach. We use a direct Principle Component Analysis (PCA) based approach as a benchmark to compare and contrast the performance of the proposed 2D-LDA approach to car MMR. We conclude that in general the 2D-LDA based algorithm supersedes the performance of the PCA based approach.

Paper Details

Date Published: 20 February 2007
PDF: 8 pages
Proc. SPIE 6496, Real-Time Image Processing 2007, 649602 (20 February 2007); doi: 10.1117/12.704592
Show Author Affiliations
I. Zafar, Loughborough Univ. (United Kingdom)
E. A. Edirisinghe, Loughborough Univ. (United Kingdom)
S. Acar, Loughborough Univ. (United Kingdom)
H. E. Bez, Loughborough Univ. (United Kingdom)

Published in SPIE Proceedings Vol. 6496:
Real-Time Image Processing 2007
Nasser Kehtarnavaz; Matthias F. Carlsohn, Editor(s)

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