Title :
Road Surface Condition Classification Based on Color and Texture Information
Author :
Zhonghua Sun ; Kebin Jia
Author_Institution :
Coll. of Electron. Inf. & Control Eng., Beijing Univ. of Technol., Beijing, China
Abstract :
Road surface condition is very important for safe driving especially in bad weather such as snow or rainy. In this paper we proposed a video camera road image status detection method. The color and texture information of the road surface is extracted from the video frame and then we build a naïve Bayesian classifier to classify the road surface image into three categories, dry, mild snow coverage, and heavy snow coverage. Meanwhile we compared the classification performance with another three popular classifiers, K-NN, Neural Network and SVM. Experimental results show that the naïve Bayesian classifier is most suitable for this classification problem.
Keywords :
Bayes methods; feature extraction; image classification; image colour analysis; image texture; K-NN; SVM; bad weather; color information; naïve Bayesian classifier; neural network; road surface condition classification; safe driving; snow coverage; texture information; video camera road image status detection method; video frame; Bayes methods; Feature extraction; Image edge detection; Roads; Snow; Support vector machines; Surface treatment; color and texture information; road surface; safe driving;
Conference_Titel :
Intelligent Information Hiding and Multimedia Signal Processing, 2013 Ninth International Conference on
Conference_Location :
Beijing
DOI :
10.1109/IIH-MSP.2013.43