Abstract
Nowadays, driver assistance systems are embedded with some expensive cars, but more importantly, those systems are not able to recognize Thai traffic signs. This paper proposes a Thai traffic sign detection and recognition system. The proposed system is implemented with two main processes: Thai traffic sign detection and recognition. For the former process, a cascade classifier trained with histogram of oriented gradient (HOG) features is used to generate a trained model for a sign detector, and then Viola-Jones cascade detector is used to classify sign and non-sign objects of the input image. For the latter process, a linear support vector machine (SVM) learner trained with HOG features is used to generate the trained model for sign symbol recognition, and then a SVM class prediction is applied for recognizing the HOG features of the detected sign. Based on a real world data-set, the proposed system can correctly detectand recognize Thai traffic signs in near real time.
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