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Road Sign Detection with Weather/Illumination Classifications and Adaptive Color Models in Various Road Images
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 Title & Authors
Road Sign Detection with Weather/Illumination Classifications and Adaptive Color Models in Various Road Images
Kim, Tae Hung; Lim, Kwang Yong; Byun, Hye Ran; Choi, Yeong Woo;
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 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.
 Keywords
Road-View Images;Road Sign Detection;Weather Classification;Illumination Classification;Adaptive Color Models;
 Language
Korean
 Cited by
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