DocumentCode :
3506364
Title :
North-American speed limit sign detection and recognition for smart cars
Author :
Mammeri, Abdelhamid ; Boukerche, Azzedine ; Jingwen Feng ; Renfei Wang
Author_Institution :
PARADISE Res. Lab., Univ. of Ottawa, Ottawa, ON, Canada
fYear :
2013
fDate :
21-24 Oct. 2013
Firstpage :
154
Lastpage :
161
Abstract :
Traffic sign detection and recognition system is becoming an essential component of smart cars. Speed-Limit Sign (SLS) is one of the most important traffic signs, since it is used to regulate the speed of vehicles in downtown and highways. The recognition of SLS by drivers is mandatory. In this paper, we investigate SLS detection and recognition system. We focus on North-American speed limit signs, including Canadian and U.S. signs. A modified version of Histogram of Oriented Gradients (HOG) is used to detect and recognize SLS through a set of two-level SVM-based classifiers. Moreover, we build our online database called North-American Speed Limit Signs (NASLS) which includes four SLS categories; white, yellow, black and orange signs. We show through an extensive set of experiments that our system achieves an accuracy of more than 94% of SLS recognition.
Keywords :
image classification; object detection; road traffic; support vector machines; Canadian traffic signs; HOG; NASLS; North-American Speed Limit Signs; North-American speed limit sign detection and recognition; SLS detection; U.S. traffic signs; histogram of oriented gradients; smart cars; traffic sign detection and recognition system; two-level SVM-based classifiers; Databases; Histograms; Image color analysis; Image segmentation; Shape; Support vector machines; Training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Local Computer Networks Workshops (LCN Workshops), 2013 IEEE 38th Conference on
Conference_Location :
Sydney, NSW
Print_ISBN :
978-1-4799-0539-3
Type :
conf
DOI :
10.1109/LCNW.2013.6758513
Filename :
6758513
Link To Document :
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