Eigen-Gradients for Traffic Sign Recognition

Sheila Esmeralda Gonzalez-Reyna, Juan Gabriel Avina-Cervantes, Sergio Eduardo Ledesma-Orozco and Ivan Cruz-Aceves

Mathematical Problems in Engineering, 2013, vol. 2013, 1-6

Abstract:

Traffic sign detection and recognition systems include a variety of applications like autonomous driving, road sign inventory, and driver support systems. Machine learning algorithms provide useful tools for traffic sign identification tasks. However, classification algorithms depend on the preprocessing stage to obtain high accuracy rates. This paper proposes a road sign characterization method based on oriented gradient maps and the Karhunen-Loeve transform in order to improve classification performance. Dimensionality reduction may be important for portable applications on resource constrained devices like FPGAs; therefore, our approach focuses on achieving a good classification accuracy by using a reduced amount of attributes compared to some state-of-the-art methods. The proposed method was tested using German Traffic Sign Recognition Benchmark, reaching a dimensionality reduction of 99.3% and a classification accuracy of 95.9% with a Multi-Layer Perceptron.

Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:364305

DOI: 10.1155/2013/364305

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