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Title: Two dimensional statistical linear discriminant analysis for real-time robust vehicle type recognition
Authors: Zafar, Iffat
Edirisinghe, Eran A.
Acar, B. Serpil
Bez, Helmut E.
Keywords: Make and model recognition (MMR)
Principle component analysis (PCA)
Linear discriminant analysis (LDA)
2D-LDA
Eigenvectors
Issue Date: 2007
Publisher: © 2007 SPIE
Citation: ZAFIR, I.... et al., 2007. Two dimensional statistical linear discriminant analysis for real-time robust vehicle type recognition. IN: Kehtarnavaz, N. and Carlsohn, M.F. (eds.) Real-Time Image Processing 2007, Proc. of SPIE-IS&T Electronic Imaging, 6496, 649602, 9pp.
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.
Description: Copyright 2007 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic electronic or print reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. This paper can also be found at: http://dx.doi.org/10.1117/12.704592
Version: Published
DOI: 10.1117/12.704592
URI: https://dspace.lboro.ac.uk/2134/6503
Appears in Collections:Conference Papers (Computer Science)

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