Road Surface Condition Classification Based on Color and Texture Information

Zhonghua Sun, Ke-bin Jia
{"title":"Road Surface Condition Classification Based on Color and Texture Information","authors":"Zhonghua Sun, Ke-bin Jia","doi":"10.1109/IIH-MSP.2013.43","DOIUrl":null,"url":null,"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.","PeriodicalId":105427,"journal":{"name":"2013 Ninth International Conference on Intelligent Information Hiding and Multimedia Signal Processing","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 Ninth International Conference on Intelligent Information Hiding and Multimedia Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IIH-MSP.2013.43","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9

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.
基于颜色和纹理信息的路面状况分类
路面状况对于安全驾驶是非常重要的,特别是在恶劣的天气,如下雪或下雨。本文提出了一种摄像机道路图像状态检测方法。从视频帧中提取路面的颜色和纹理信息,然后构建naïve贝叶斯分类器,将路面图像分为干燥、轻度积雪和重度积雪三类。同时,我们比较了另外三种流行的分类器,K-NN,神经网络和支持向量机的分类性能。实验结果表明naïve贝叶斯分类器最适合该分类问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信