In order to be deployed in driving environments, Intelligent transport system (ITS) must be able to
recognize and respond to exceptional road conditions such as traffic signs, highway work zones and
imminent road works automatically. Recognition of traffic sign is playing a vital role in the intelligent
transport system, it enhances traffic safety by providing drivers with safety and precaution information
about road hazards. To recognize the traffic sign, the system has been proposed with three phases. They
are Traffic board Detection, Feature extraction and Recognition. The detection phase consists of RGBbased
colour thresholding and shape analysis, which offers robustness to differences in lighting situations.
A Histogram of Oriented Gradients (HOG) technique was adopted to extract the features from the
segmented output. Finally, traffic signs recognition is done by k-Nearest Neighbors (k-NN) classifiers. It
achieves an classification accuracy upto 63%.
%0 Journal Article
%1 karthigapl2016trafficsign
%A Karthiga.PL,
%A Roomi, S.Md.Mansoor
%A Kowsalya.J,
%D 2016
%J Computer Science & Engineering: An International Journal (CSEIJ)
%K Intelligent System Transport
%N 01
%P 09
%R 10.5121/cseij.2016.6102
%T Traffic-Sign Recognition For An Intelligent Vehicle/Driver Assistant System Using HOG
%U http://airccse.org/journal/cseij/index.html
%V 06
%X In order to be deployed in driving environments, Intelligent transport system (ITS) must be able to
recognize and respond to exceptional road conditions such as traffic signs, highway work zones and
imminent road works automatically. Recognition of traffic sign is playing a vital role in the intelligent
transport system, it enhances traffic safety by providing drivers with safety and precaution information
about road hazards. To recognize the traffic sign, the system has been proposed with three phases. They
are Traffic board Detection, Feature extraction and Recognition. The detection phase consists of RGBbased
colour thresholding and shape analysis, which offers robustness to differences in lighting situations.
A Histogram of Oriented Gradients (HOG) technique was adopted to extract the features from the
segmented output. Finally, traffic signs recognition is done by k-Nearest Neighbors (k-NN) classifiers. It
achieves an classification accuracy upto 63%.
@article{karthigapl2016trafficsign,
abstract = {In order to be deployed in driving environments, Intelligent transport system (ITS) must be able to
recognize and respond to exceptional road conditions such as traffic signs, highway work zones and
imminent road works automatically. Recognition of traffic sign is playing a vital role in the intelligent
transport system, it enhances traffic safety by providing drivers with safety and precaution information
about road hazards. To recognize the traffic sign, the system has been proposed with three phases. They
are Traffic board Detection, Feature extraction and Recognition. The detection phase consists of RGBbased
colour thresholding and shape analysis, which offers robustness to differences in lighting situations.
A Histogram of Oriented Gradients (HOG) technique was adopted to extract the features from the
segmented output. Finally, traffic signs recognition is done by k-Nearest Neighbors (k-NN) classifiers. It
achieves an classification accuracy upto 63%.
},
added-at = {2017-08-26T07:55:58.000+0200},
author = {Karthiga.PL and Roomi, S.Md.Mansoor and Kowsalya.J},
biburl = {https://www.bibsonomy.org/bibtex/217e9e0b602b2c6cce8548c5ea0149762/marliner},
doi = {10.5121/cseij.2016.6102},
interhash = {a4e0c2e9253c5e155e99b911916708e7},
intrahash = {17e9e0b602b2c6cce8548c5ea0149762},
issn = {2231329X},
journal = {Computer Science & Engineering: An International Journal (CSEIJ) },
keywords = {Intelligent System Transport},
month = {February 2016},
number = 01,
pages = 09,
timestamp = {2017-08-26T07:55:58.000+0200},
title = {Traffic-Sign Recognition For An Intelligent Vehicle/Driver Assistant System Using HOG},
url = {http://airccse.org/journal/cseij/index.html},
volume = 06,
year = 2016
}