Earticle

초록

영어

In general, road lane detection from a traffic surveillance camera is done by the analysis of geometric shapes of the road. Thus, Hough transform or B-snake technology is preferred to intelligent pattern matching or machine learning such as neural network. However, we insist that the feasibility of using intelligent technique in this area is quite undervalued. In this paper, we first divide the image into halves and use only the lower part in detection and binarize them by analyzing RGB channel. Then the boundary lines are extracted by applying 4-directional contour tracking algorithm and vectors with distance and angle values are extracted from those boundary lines to use as input for fuzzy C-means clustering algorithm. Clustered vectors form a straight line for road lanes by our method. In experiment, the proposed intelligent method is slightly slower than Hough transform but better in accuracy thus there is a room for intelligent method such as fuzzy C-means to solve this problem.

목차

Abstract
 1. Introduction
 2. Lane Detection using Fuzzy C-Means Clustering
 3. Experimental Results and Analysis
 4. Conclusions
 References

키워드

저자

• Kwang Baek Kim [ Department of Computer Engineering, Silla University, Korea ] Corresponding author
• Doo Heon Song [ Department of Computer Games, Yong-in SongDam University, Korea ]
• Jae-Hyun Cho [ Department of Computer Engineering, Catholic University of Pusan, Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605