Detection and classification of road signs for automatic inventory systems using computer vision

Authors:
Gustavo A. Peláez Coronado;María Romero Muòoz;José María Armingol;Arturo de la Escalera;Juan Jesús Muòoz;Wouter van Bijsterveld;Juan Antonio Bolaòo
Affiliations:
Systems Engineering and Automation Department, Intelligent Systems Laboratory, University Carlos III of Madrid, Leganés, Spain;Systems Engineering and Automation Department, Intelligent Systems Laboratory, University Carlos III of Madrid, Leganés, Spain;Systems Engineering and Automation Department, Intelligent Systems Laboratory, University Carlos III of Madrid, Leganés, Spain;Systems Engineering and Automation Department, Intelligent Systems Laboratory, University Carlos III of Madrid, Leganés, Spain;Geotecnia y Cimientos S.A. Coslada, Madrid, Spain;Geotecnia y Cimientos S.A. Coslada, Madrid, Spain;Geotecnia y Cimientos S.A. Coslada, Madrid, Spain
Venue:
Integrated Computer-Aided Engineering
Year:
2012

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Abstract

This article describes an intelligent system that enables the automatic recognition of road signs from image sequences in road environments. The main difficulties the system has to deal with are related to changes in lighting conditions, obstacles blocking the view, the presence of objects with geometric and chromatic similarities and the absence of previous knowledge about their position and orientation. The application of different techniques allows the system to overcome this variety of problems. Therefore, the road sign recognition system is based on a first pre-processing of images, making use of information about road geometry to isolate those areas of the image where road signs may appear. The detection step uses colour and shape analysis to determine the regions of the image where potential road signs may be located. A third step focuses on recognition and classification using pattern matching and edge feature analysis. The proposed algorithm has been tested in different weather and lighting conditions and roads one and two-lane roads and motorways, overall, a total of 1200 kilometres with a very high success rate of detection and classification as the experimental results show.