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 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 yielded fast recognition rate and is more adequate for real-time application as well.
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