An Intelligent Lane Markers Recognition and Localization System Using Improved Hough Transform

Article Preview

Abstract:

According to the U.S. National Highway Traffic Safety Administration, single vehicle road departures result in many serious accidents each year[1]. An intelligent lane markers recognition and localization system can assist vehicles stay in proper location of a lane that will reduce possibility of car accidents correspondingly. A great deal of lane recognition algorithms have been developed over the past several decades. However, reliable detection is still an issue because of variable road face conditions. An optimum algorithm, Probabilistic Hough Transform (PHT) is presented in this paper for intelligent Lane markers recognition which use a fixed camera installed on the vehicle to transmit video information. The result of experiment proved that under inconsistent illumination and a diversity of road conditions, the accuracy and efficiency of developed system have been improved greatly.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

1186-1190

Citation:

Online since:

October 2011

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2012 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Linda Webb Using Model-Based Design to Develop and Deploy a Video Processing Application.

Google Scholar

[2] an 2007. Kim H., Hong S., Son H., Roska T., and Werblin, F., High speed road boundary detection on the images for autonomous vehicle with multi-layer CNN, Proc. IEEE Internet Symposium on Circuits and Systems, pp.769-772, (2003).

DOI: 10.1109/iscas.2003.1206426

Google Scholar

[3] D. J., Kang and M. H., Jung, Road lane segmentation using dynamic programming for active safety vehicles, Pattern Recognition Letters 24, pp.3177-3185, (2003).

DOI: 10.1016/j.patrec.2003.08.003

Google Scholar

[4] A., Broggi and S., Berte, Vision-based road detection in automotive systems: a real-time expectation driven approach,J. Artificial Intelligence Res. 3, pp.325-348, (1995).

DOI: 10.1613/jair.185

Google Scholar

[5] M., Kazui, M., Haseyama, and Kitajima, H., Estimation of the vanishing point for automatic driving system using a crossratio Systems and Computer , Japan, Vol. 33, No. 9, pp.31-39, (2002).

DOI: 10.1002/scj.10090

Google Scholar

[6] J.W., Lee, A machine vision system for lane departure detection, Computer Vision and Image Understanding 86 (1), p.52–78, (2002).

DOI: 10.1006/cviu.2002.0958

Google Scholar

[7] Suzuki A., Yasui N., Kaneko M., Lane Recognition System for Guiding of Autonomous Vehicle", Intelligent Vehicle '92, pp.196-201, Sept. 2000. Suzuki A., Yasui N., Kaneko M., "Lane Recognition System for Guiding of Autonomous Vehicle", Intelligent Vehicle , 92, pp.196-201, Sept. (2000).

DOI: 10.1109/ivs.1992.252256

Google Scholar

[8] Lane detection using color-based segmentation. intelligent Vehicles], June 2005. By K. Y. Chiu and S. F. Lin.

Google Scholar

[9] Pratt W.K. Digital Image Processing. New York: John Wiley & Sons, (1991).

Google Scholar

[10] Fu mengyin, Li bo, Wang meiling, an intelligence lane detection algorithm based on inverse perspective mapping, 2008, 34(3): 368-371.

Google Scholar

[11] J. Illingworth, J. Kittler, A survey of the Hough transform, Comput. Vision Graphics Image Process. 44 (1988) 87-116.

DOI: 10.1016/s0734-189x(88)80033-1

Google Scholar

[12] N. Kiryati, Y. Eldar, A.M. Bruckstein, A probabilistic Hough transform, Pattern Recognition 24(4) (1991) 303-316.

DOI: 10.1016/0031-3203(91)90073-e

Google Scholar