RT info:eu-repo/semantics/article T1 Text detection and recognition on traffic panels from street-level imagery using visual appearance A1 González Arroyo, Álvaro A1 Bergasa Pascual, Luis Miguel A1 Yebes Torres, José Javier K1 Bag of Visual Words (BOVW) K1 Computer vision K1 Traffic panel detection K1 Traffic panel inventory K1 Traffic panel recognition K1 Electrónica K1 Electronics AB Traffic sign detection and recognition has been thoroughly studied for a long time. However, traffic panel detection and recognition still remains a challenge in computer vision due to its different types and the huge variability of the information depicted in them. This paper presents a method to detect traffic panels in street-level images and to recognize the information contained on them, as an application to intelligent transportation systems (ITS). The main purpose can be to make an automatic inventory of the traffic panels located in a road to support road maintenance and to assist drivers. Our proposal extracts local descriptors at some interest keypoints after applying blue and white color segmentation. Then, images are represented as a “bag of visual words” and classified using Naïve Bayes or support vector machines. This visual appearance categorization method is a new approach for traffic panel detection in the state of the art. Finally, our own text detection and recognition method is applied on those images where a traffic panel has been detected, in order to automatically read and save the information depicted in the panels. We propose a language model partly based on a dynamic dictionary for a limited geographical area using a reverse geocoding service. 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 ITS. PB IEEE SN 1524-9050 YR 2014 FD 2014-02-01 LK http://hdl.handle.net/10017/43229 UL http://hdl.handle.net/10017/43229 LA eng NO Ministerio de Economía y Competitividad DS MINDS@UW RD 28-mar-2024