Traffic panels detection using visual appearance

Traffic signs detection has been thoroughly studied for a long time. However, road panels detection still remains a challenge in computer vision due to the huge variability of types of traffic panels, as the information depicted in them is not restricted. This paper presents a method to detect traffic panels in street-level images as an application to Intelligent Transportation Systems (ITS), since the main purpose can be to make an automatic inventory of the traffic panels located in a road to support maintenance and to assist drivers in order to improve human quality of life. The proposed method extracts local descriptors at some interest points after applying a color detection method for blue and white pixels. Then, the images are modeled using a Bag of Visual Words technique and classified using Naïve Bayes theory and SVM. Experimental results on real images from Google Street View prove the efficiency of the proposed method and give way to using street-level images for different applications on robotics and ITS.