Abstract
Traffic Signs provide visual information to drivers, in order to warn them from possible danger on the road, set rules for pedestrian protection and inform people about their environment, to name a few. Therefore, Traffic Sign Detection and Recognition Systems have increased their interest in the scientific community. Applications include autonomous driving systems, road sign inventory and driver support assistance systems. This paper presents a traffic sign recognition algorithm for velocity signs, based on Linear Discriminant Analysis that performs dimensionality reduction and it improves class separability. The tests were performed on the German Traffic Sign Recognition Benchmark, using a Multi-Layer Perceptron as a classification tool. LDA classification and k-Nearest Neighbors were also used for comparison. Experimental results demonstrate the validity of the proposed approach, having a 99.1% of attributes reduction and a 96.5% of classification accuracy.
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Gonzalez-Reyna, S.E., Avina-Cervantes, J.G., Ledesma-Orozco, S.E., Cruz-Aceves, I., de Guadalupe Garcia-Hernandez, M. (2013). Traffic Sign Recognition Based on Linear Discriminant Analysis. In: Castro, F., Gelbukh, A., González, M. (eds) Advances in Soft Computing and Its Applications. MICAI 2013. Lecture Notes in Computer Science(), vol 8266. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45111-9_16
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DOI: https://doi.org/10.1007/978-3-642-45111-9_16
Publisher Name: Springer, Berlin, Heidelberg
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