by Jiang, R, Klette, R, Vaudrey, T and Wang, S
Abstract:
Particle filtering of boundary points is a robust way to estimate lanes. This paper introduces a new lane model in correspondence to this particle filter-based approach, which is flexible to detect all kinds of lanes. A modified version of an Euclidean distance transform is applied to an edge map of a road image from a birds-eye view to provide information for boundary point detection. An efficient lane tracking method is also discussed. The use of this distance transform exploits useful information in lane detection situations, and greatly facilitates the initialization of the particle filter, as well as lane tracking. Finally, the paper validates the algorithm with experimental evidence for lane detection and tracking. © 2009 Springer Berlin Heidelberg.
Reference:
New Lane model and distance transform for lane detection and tracking (Jiang, R, Klette, R, Vaudrey, T and Wang, S), In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 5702 LNCS, 2009.
Bibtex Entry:
@inproceedings{jiang2009newtracking,
author = "Jiang, R and Klette, R and Vaudrey, T and Wang, S",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "1044--1052",
title = "New Lane model and distance transform for lane detection and tracking",
volume = "5702 LNCS",
year = "2009",
abstract = "Particle filtering of boundary points is a robust way to estimate lanes. This paper introduces a new lane model in correspondence to this particle filter-based approach, which is flexible to detect all kinds of lanes. A modified version of an Euclidean distance transform is applied to an edge map of a road image from a birds-eye view to provide information for boundary point detection. An efficient lane tracking method is also discussed. The use of this distance transform exploits useful information in lane detection situations, and greatly facilitates the initialization of the particle filter, as well as lane tracking. Finally, the paper validates the algorithm with experimental evidence for lane detection and tracking. © 2009 Springer Berlin Heidelberg.",
doi = "10.1007/978-3-642-03767-2_127",
isbn = "3642037666",
isbn = "9783642037665",
issn = "0302-9743",
eissn = "1611-3349",
language = "eng",
}