Share Email Print

Proceedings Paper

Vision-based real-time road detection in urban traffic
Author(s): Jianye Lu; Ming Yang; Hong Wang; Bo Zhang
Format Member Price Non-Member Price
PDF $17.00 $21.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Road detection is the major task of autonomous vehicle guidance. We notice that feature lines, which are parallel to the road boundaries, are reliable cues for road detection in urban traffic. Therefore we present a real-time method that extracts the most likely road model using a set of feature-line-pairs (FLPs). Unlike the traditional methods that extract a single line, we extract the feature lines in pairs. Working with a linearly parameterized road model, FLP appears some geometrical consistency, which allows us to detect each of them with a Kalman filter tracking scheme. Since each FLP determines a road model, we apply regression diagnostics technique to robustly estimate the parameters of the whole road model from all FLPs. Another Kalman filter is used to track road model from frame to frame to provide a more precise and more robust detection result. Experimental results in urban traffic demonstrate real-time processing ability and high robustness.

Paper Details

Date Published: 27 February 2002
PDF: 8 pages
Proc. SPIE 4666, Real-Time Imaging VI, (27 February 2002); doi: 10.1117/12.458518
Show Author Affiliations
Jianye Lu, Tsinghua Univ. (China)
Ming Yang, Tsinghua Univ. (China)
Hong Wang, Tsinghua Univ. (China)
Bo Zhang, Tsinghua Univ. (China)

Published in SPIE Proceedings Vol. 4666:
Real-Time Imaging VI
Nasser Kehtarnavaz, Editor(s)

© SPIE. Terms of Use
Back to Top