A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
Road sign classification using Laplace kernel classifier
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
Traffic Sign Detection and Pattern Recognition Using Support Vector Machine
ICAPR '09 Proceedings of the 2009 Seventh International Conference on Advances in Pattern Recognition
A road sign recognition system based on dynamic visual model
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Road-Sign Detection and Recognition Based on Support Vector Machines
IEEE Transactions on Intelligent Transportation Systems
Expert Systems with Applications: An International Journal
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This paper presents a new approach for color detection and segmentation based on Support Vector Machine (SVM) to retrieve candidate regions of traffic signs in real-time video processing. Instead of processing on each pixel, this approach utilizes a block of pixels as an input vector of SVM for color classification, where the dimension of each vector can be extended by a group of neighboring pixels. This helps to handle the diversification of data on both training and testing samples. After that, Hough transform and contour detection are applied to verify the candidate regions by detecting shapes of circle and triangle. The experimental results are highly accurate and robust for our testing database, where samples are recorded on various states of environment.