Traffic sign recognition using Scale Invariant Feature Transform and color classification

In this paper, we propose a traffic sign detection and recognition technique by augmenting the Scale Invariant Feature Transform (SIFT) with new features related to the color of local regions. SIFT finds local invariant features in a given image and matches these features to the features of images that exist in the training set. Recognition is performed by finding out the training image that gives the maximum number of matches. In this study, performance of SIFT in traffic sign detection and recognition issue is investigated. Afterwards, new features which increase the performance are added. Those are color inspection by using proposed color classification method and inspecting the orientations of SIFT features. These features check the accuracy of matches which are found by SIFT. Color classification method finds out true colors of the pixels by applying some classification rules. It is observed that adding color and orientation inspections raises the recognition performance of SIFT significantly. Obtained results are very good and satisfying even for the images containing traffic signs which are rotated, have undergone affine transformations, have been damaged, occluded, overshadowed, had alteration in color, pictured in different weather conditions and different illumination conditions.