scholarly journals Traffic Sign Recognition Method Integrating Multi-Layer Features and Kernel Extreme Learning Machine Classifier

2019 ◽  
Vol 60 (1) ◽  
pp. 147-161 ◽  
Author(s):  
Wei Sun ◽  
Hongji Du ◽  
Xiaorui Zhang ◽  
Xiaozheng He
Keyword(s):  
Extreme Learning Machine ◽  
Traffic Sign Recognition ◽  
Recognition Method ◽  
Traffic Sign ◽  
Sign Recognition ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

Traffic Sign Recognition Based on SIFT Features

2010 ◽  
Vol 121-122 ◽  
pp. 596-599 ◽  
Author(s):  
Ni An Cai ◽  
Wen Zhao Liang ◽  
Shao Qiu Xu ◽  
Fang Zhen Li
Keyword(s):  
Traditional Method ◽  
Euclidean Distance ◽  
Experimental Results ◽  
Traffic Sign Recognition ◽  
Recognition Method ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Recognition ◽  
Good Ability ◽  
Sift Features

A recognition method for traffic signs based on an SIFT features is proposed to solve the problems of distortion and occlusion. SIFT features are first extracted from traffic signs and matched by using the Euclidean distance. Then the recognition is implemented based on the similarity. Experimental results show that the proposed method, superior to traditional method, can excellently recognize traffic signs with the transformation of scale, rotation, and distortion and has a good ability of anti-noise and anti-occlusion.

Traffic sign recognition method for intelligent vehicles

2018 ◽  
Vol 35 (11) ◽  
pp. 1907 ◽  
Author(s):  
Ayoub Ellahyani ◽  
Mohamed El Ansari ◽  
Redouan Lahmyed ◽  
Alain Trémeau
Keyword(s):  
Intelligent Vehicles ◽  
Traffic Sign Recognition ◽  
Recognition Method ◽  
Traffic Sign ◽  
Sign Recognition
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