Sesmero Lorente, María PazAlonso Weber, Juan ManuelGutiérrez Sánchez, GermánLedezma Espino, Agapito IsmaelSanchis de Miguel, María Araceli2011-01-112011-01-112007Computational and ambient intelligence: 9th International Work-Conference on Artificial Neural Networks, IWANN 2007. Springer, 2007, pp. 733-740978-3-540-73006-40302-9743https://hdl.handle.net/10016/9937Proceeding of: 9th International Work-Conference on Artificial Neural Networks, IWANN 2007, San Sebastían, España, junio, 2007.Several complex problems have to be solved in order to build Advanced Driving Assistance Systems. Among them, an important problem is the detection and classification of traffic signs, which can appear at any position within a captured image. This paper describes a system that employs independent modules to classify several prohibition road signs. Combining the predictions made by the set of classifiers, a unique final classification is achieved. To reduce the computational complexity and to achieve a real-time system, a previous input feature selection is performed. Experimental evaluation confirms that using this feature selection allows a significant input data reduction without an important loss of output accuracy.application/pdfengSpringer-Verlag Berlin HeidelbergTraffic Sign RecognitionArtificial Neural NetworksFeature SelectionBinary Classifierconference paperInformática10.1007/978-3-540-73007-1_88open access733740Computational and ambient intelligence: 9th International Work-Conference on Artificial Neural Networks, IWANN 20074507