1. Traffic Sign Recognition Based On Multi-feature Fusion and ELM Classifier
- Author
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Saouli Aziz, Fakhri Youssef, and El Aroussi Mohamed
- Subjects
Local binary patterns ,Computer science ,business.industry ,020207 software engineering ,Pattern recognition ,02 engineering and technology ,ComputingMethodologies_PATTERNRECOGNITION ,Multi feature fusion ,Histogram of oriented gradients ,Discriminative model ,0202 electrical engineering, electronic engineering, information engineering ,General Earth and Planetary Sciences ,Traffic sign recognition ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,Classifier (UML) ,General Environmental Science ,Extreme learning machine - Abstract
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.
- Published
- 2018