Road Sign Detection with Weather/Illumination Classifications and Adaptive Color Models in Various Road Images


KIPS Transactions on Software and Data Engineering, Vol. 4, No. 11, pp. 521-528, Nov. 2015
10.3745/KTSDE.2015.4.11.521,   PDF Download:

Abstract

Road-view object classification methods are mostly influenced by weather and illumination conditions, thus the most of the research activities are based on dataset in clean weathers. In this paper, we present a road-view object classification method based on color segmentation that works for all kinds of weathers. The proposed method first classifies the weather and illumination conditions and then applies the weather-specified color models to find the road traffic signs. Using 5 different features of the road-view images, we classify the weather and light conditions as sunny, cloudy, rainy, night, and backlight. Based on the classified weather and illuminations, our model selects the weather-specific color ranges to generate Gaussian Mixture Model for each colors, Green, Yellow, and Blue. The proposed method successfully detects the traffic signs regardless of the weather and illumination conditions.


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Cite this article
[IEEE Style]
T. H. Kim, K. Y. Lim, H. R. Byun, Y. W. Choi, "Road Sign Detection with Weather/Illumination Classifications and Adaptive Color Models in Various Road Images," KIPS Transactions on Software and Data Engineering, vol. 4, no. 11, pp. 521-528, 2015. DOI: 10.3745/KTSDE.2015.4.11.521.

[ACM Style]
Tae Hung Kim, Kwang Yong Lim, Hye Ran Byun, and Yeong Woo Choi. 2015. Road Sign Detection with Weather/Illumination Classifications and Adaptive Color Models in Various Road Images. KIPS Transactions on Software and Data Engineering, 4, 11, (2015), 521-528. DOI: 10.3745/KTSDE.2015.4.11.521.