Principal component analysis for speed limit Traffic Sign Recognition

Author

Perez-Perez, Sergio Eduardo ; Gonzalez-Reyna, Sheila Esmeralda ; Ledesma-Orozco, Sergio Eduardo ; Avina-Cervantes, J.G.

Author_Institution

Div. de Ingenierias Campus Irapuato-Salamanca, Univ. de Guanajuato, Salamanca, Mexico

fYear

2013

fDate

13-15 Nov. 2013

Firstpage

1

Lastpage

5

Abstract

Traffic Sign Recognition has recently become a popular research field. The main applications include Autonomous Driving Systems, Driver Assistance Systems, road sign inventory, to name a few. In this paper, a speed limit traffic sign recognition system is proposed based on Principal Component Analysis (PCA) with preprocessing steps, that help on perspective and extreme luminance variation correction. The classification is performed by a feed-forward neural network, or Multi-Layer Perceptron (MLP). Experimental results present a classification accuracy similar to some state of the art systems, but with a more compact scheme.

Keywords

driver information systems; image classification; intelligent transportation systems; multilayer perceptrons; object recognition; principal component analysis; traffic engineering computing; MLP; PCA; autonomous driving systems; classification; driver assistance systems; extreme luminance variation correction; feed-forward neural network; multilayer perceptron; perspective variation; principal component analysis; road sign inventory; speed limit traffic sign recognition system; Accuracy; Histograms; Image color analysis; Image segmentation; Lighting; Principal component analysis; Training; Driver Assistance Systems; Feature Extraction; Multi-Layer Perceptron; Principal Component Analysis; Traffic Sign Recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Power, Electronics and Computing (ROPEC), 2013 IEEE International Autumn Meeting on

Conference_Location

Mexico City

Type

conf

DOI

10.1109/ROPEC.2013.6702716

Filename

6702716