Fusion of Local and Global Feature Extraction Based on Uniform LBP and DCT for Traffic Sign Recognition

Samira El Margae; Mounir Ait Kerroum; Youssef Fakhri

doi:10.15866/irecos.v10i1.5051

Fusion of Local and Global Feature Extraction Based on Uniform LBP and DCT for Traffic Sign Recognition

Samira El Margae^(1*), Mounir Ait Kerroum⁽²⁾, Youssef Fakhri⁽³⁾

^(*) Corresponding author

Authors' affiliations

DOI: https://doi.org/10.15866/irecos.v10i1.5051

Abstract

Automatic traffic sign recognition has been converted to a real challenge for high performance of computer vision and machine learning techniques. It is an important issue, in particular for vehicle safety applications. In this paper the fusion of two discriminative and complementary feature sets, i.e., the Discrete Cosine Transform and the Uniform block based Local Binary Pattern has been presented. The DCT descriptor is used to extract the global gray-scale features of the whole image whereas the ULBP descriptor is able to capture the local gray-scale features of the traffic sign image and are insensitive to illumination variations which contributes most to traffic sign recognition. Principal Component Analysis is used for dimensionality reduction. We have analyzed the performance of this method under different distance classifiers like City-block and Euclidean distances. Experimental results on GTSRB and BTSC databases show that the proposed fusion method has an obvious performance improvement compared with the other classical recognition methods.
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Keywords

Traffic Sign Recognition; Discrete Cosine Transform; Local Binary Pattern; Minimum Distance Classifier; Fusion

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