Abstract
The tasks of traffic signs are notifying drivers about the current state of the road and giving them other important information for navigation. In this paper, a new approach for detection, tracking, and recognition such objects is presented. Road signs are detected using color thresholding, after that candidate blobs that have specific criteria are classified based on their geometrical shape and are tracked trough successive frames based on a new similarity measure. Candidate blobs that successfully pass the tracking module are processed for extracting their fractal features, and final recognition is done based on support vector machines with kernel function. Results validate effectiveness of newly employed fractal feature and show high accuracy with a low false hit rate of this method and its robustness to illumination changes and road sign occlusion or scale changes. Also results indicate that compared to the other pictogram feature representation techniques, this approach shows a more proper description of road signs.
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