Lane detection and tracking using B-Snake

Yue Wang, Eam Khwang Teoh, Dinggang Shen

Research output: Contribution to journalArticle

572 Citations (Scopus)

Abstract

In this paper, we proposed a B-Snake based lane detection and tracking algorithm without any cameras' parameters. Compared with other lane models, the B-Snake based lane model is able to describe a wider range of lane structures since B-Spline can form any arbitrary shape by a set of control points. The problems of detecting both sides of lane markings (or boundaries) have been merged here as the problem of detecting the mid-line of the lane, by using the knowledge of the perspective parallel lines. Furthermore, a robust algorithm, called CHEVP, is presented for providing a good initial position for the B-Snake. Also, a minimum error method by Minimum Mean Square Error (MMSE) is proposed to determine the control points of the B-Snake model by the overall image forces on two sides of lane. Experimental results show that the proposed method is robust against noise, shadows, and illumination variations in the captured road images. It is also applicable to the marked and the unmarked roads, as well as the dash and the solid paint line roads.

Original languageEnglish
Pages (from-to)269-280
Number of pages12
JournalImage and Vision Computing
Volume22
Issue number4
DOIs
Publication statusPublished - 2004 Apr 1
Externally publishedYes

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Paint
Mean square error
Splines
Lighting
Cameras

Keywords

  • B-Spline
  • Intelligent vehicle
  • Lane detection
  • Lane model
  • Machine vision
  • Snake

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Lane detection and tracking using B-Snake. / Wang, Yue; Teoh, Eam Khwang; Shen, Dinggang.

In: Image and Vision Computing, Vol. 22, No. 4, 01.04.2004, p. 269-280.

Research output: Contribution to journalArticle

Wang, Yue ; Teoh, Eam Khwang ; Shen, Dinggang. / Lane detection and tracking using B-Snake. In: Image and Vision Computing. 2004 ; Vol. 22, No. 4. pp. 269-280.
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