Traffic Sign Recognition Based On Multi-feature Fusion and ELM Classifier

This paper proposes a novel and efficient method for traffic sign recognition based on combination of complementary and discriminative feature sets. The extracted features are the histogram of oriented gradients (HOG) feature, Gabor feature and Compound local binary pattern (CLBP) feature. The class...

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Bibliographic Details
Main Authors: Aziz, Saouli, Mohamed, El Aroussi, Youssef, Fakhri
Format: Conference Proceeding
Language:English
Subjects:
Compound Local Binary Pattern
Extreme learning machine
Gabor filter
Histogram of oriented gradients
Traffic sign recognition
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Summary:This paper proposes a novel and efficient method for traffic sign recognition based on combination of complementary and discriminative feature sets. The extracted features are the histogram of oriented gradients (HOG) feature, Gabor feature and Compound local binary pattern (CLBP) feature. The classification is performed using the extreme learning machine (ELM) algorithm. Performances of the proposed approach are evaluated on both German Traffic Sign Recognition Benchmark (GTSRB) and Belgium Traffic Sign Classification (BTSC) Datasets respectively. The results of the experimental work demonstrate that each feature yields fairly high accuracy and the combination of three features has shown good complementariness and yielded fast recognition rate and is more adequate for real-time application as well.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2018.01.109