Driving Supportive System for Warning Traffic Sign Classification
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2014, Vol 16, Issue 4
Abstract
Abstract: Traffic signs should be accurately identified in order to prevent vital road accidents and secure lives. The objective of this paper is to detect the warning traffic signs and recognize the message it is designed to convey. This system is based on extracting the warning sign from the traffic scene by Windowed Hough Transform. Next Histogram Oriented Gradient (HOG) is used to collect the feature of the extracted part of triangular object and finally SVM classifier is applied to train the HOG features. To summarize, the system first detects the warning traffic sign in the first place, specifies whether the detected sign is a warning sign, and then determines the meaning of the symbol inside it. The SVM classifier was trained with 200 images which were collected in different light conditions. To check the robustness of this system, it was tested against 327 images which contain 292 warning traffic sign and 35 other types of traffic signs. It was found that the accuracy of recognition was approximately 94% which indicates clearly the high robustness targeted by this system.
Authors and Affiliations
Moumita Roy Tora, , Rubel Biswas
Keywords
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- EP ID EP162986
- DOI 10.9790/0661-16473544
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