• 综合
  • 标题
  • 关键词
  • 摘要
  • 学者
  • 期刊-刊名
  • 期刊-ISSN
  • 会议名称
搜索

作者:

Song, Hongjun (Song, Hongjun.) | Chen, Yangzhou (Chen, Yangzhou.) (学者:陈阳舟) | Gao, Yuanyuan (Gao, Yuanyuan.)

收录:

EI Scopus

摘要:

A real time traffic meteorological visibility distance evaluation algorithm in foggy weather by using dark channel prior and lane detection methodology is proposed in this paper. In foggy image, dark channel prior directly provides accurate transmission estimation. A novel lane detection algorithm which is called variable box search (VBS) is proposed in this paper. This novel algorithm only needs little running time and could maintain real time procession. Background generating and updating method which is called Gaussian mixture model (GMM) will be used to get clear background image. Two endpoints of one traffic lane are marked and saved; these data will be served for traffic scene distance calculation. Extinction coefficient k could be calculated by these two end points transmission division based on the monocular model and dark channel prior. Finally, the meteorological visibility will be according to definition from International Commission on Illumination. According to the traditional fog sorting methodology, we fulfil fog category method by our algorithm based on the extinction coefficient value. Experimental data are taken from the actual traffic scene and network data. Experimental results verify the effectiveness of this proposed algorithm. Copyright © 2015 Inderscience Enterprises Ltd.

关键词:

Gaussian distribution Visibility

作者机构:

  • [ 1 ] [Song, Hongjun]Beijing University of Technology, Beijing, BJ; 100124, China
  • [ 2 ] [Chen, Yangzhou]Beijing University of Technology, Beijing, BJ; 100124, China
  • [ 3 ] [Gao, Yuanyuan]Zhejiang A and F University, Hangzhou, HZ; Zhejiang; 311300, China

通讯作者信息:

  • [song, hongjun]beijing university of technology, beijing, bj; 100124, china

电子邮件地址:

查看成果更多字段

相关关键词:

相关文章:

来源 :

ISSN: 1742-7185

年份: 2015

期: 4

卷: 10

页码: 375-386

被引次数:

WoS核心集被引频次: 0

SCOPUS被引频次: 10

ESI高被引论文在榜: 0 展开所有

万方被引频次:

中文被引频次:

近30日浏览量: 0

归属院系:

在线人数/总访问数:571/4955291
地址:北京工业大学图书馆(北京市朝阳区平乐园100号 邮编:100124) 联系我们:010-67392185
版权所有:北京工业大学图书馆 站点建设与维护:北京爱琴海乐之技术有限公司