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http://dx.doi.org/10.5762/KAIS.2014.15.9.5446

Study of Traffic Sign Auto-Recognition  

Kwon, Mann-Jun (Dept. of Automotive Engineering, Ajou Motor College)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.15, no.9, 2014 , pp. 5446-5451 More about this Journal
Abstract
Because there are some mistakes by hand in processing electronic maps using a navigation terminal, this paper proposes an automatic offline recognition for traffic signs, which are considered ingredient navigation information. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), which have been used widely in the field of 2D face recognition as computer vision and pattern recognition applications, was used to recognize traffic signs. First, using PCA, a high-dimensional 2D image data was projected to a low-dimensional feature vector. The LDA maximized the between scatter matrix and minimized the within scatter matrix using the low-dimensional feature vector obtained from PCA. The extracted traffic signs under a real-world road environment were recognized successfully with a 92.3% recognition rate using the 40 feature vectors created by the proposed algorithm.
Keywords
LDA; Navigation; PCA; Traffic Sign;
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Times Cited By KSCI : 2  (Citation Analysis)
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