%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 }