By Topic

Traffic Sign Detection and Pattern Recognition Using Support Vector Machine

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Kiran C. G. ; Network Syst. & Technol. (P) Ltd., Thiruvananthapuram ; Lekhesh V. Prabhu ; Abdu Rahiman V. ; Rajeev K.

A vision based vehicle guidance system must be able to detect and recognize traffic signs. Traffic sign recognition systems collect information about road signs and helps the driver to make timely decisions, making driving safer and easier. This paper deals with the detection and recognition of traffic signs from image sequences using the colour information. Colour based segmentation techniques are employed for traffic sign detection. In order to improve the performance of segmentation, we used the product of enhanced hue and saturation components. To obtain better shape classification performance, we used linear support vector machine with the distance to border features of the segmented blobs. Recognition of traffic signs are implemented using multi-classifier non-linear support vector machine with edge related pixels of interest as the feature.

Published in:

Advances in Pattern Recognition, 2009. ICAPR '09. Seventh International Conference on

Date of Conference:

4-6 Feb. 2009