Paper Info
Title
A superparticle filter for lane detection
Abstract
We extend previously defined particle filters for lane detection by using a more general lane model supporting the use of two independent particle filters for detecting left and right lane borders separately, by combining multiple particles, traditionally used for identifying a winning particle in one image row, into one superparticle, and by using local linear regression for adjusting detected border points. The combination of multiple particles makes it possible to extend the traditional emphasis of particle-filter-based lane detectors (on identifying sequences of isolated border points) towards a local approximation of lane borders by polygonal or smooth curves further detailed in our local linear regression. The paper shows by experimental studies that results, obtained by the proposed novel lane detection procedure, improve compared to previously achieved particle-filter-based results especially for challenging lane detection situations. The presentation of several methods for comparative performance evaluation is another contribution of this paper. HighlightsExtends previously defined particle filters for lane detection.Introduces a more general lane model.Applies two independent particle filters for left and right lane borders.Combines multiple row-particles into one superparticle.Provides an extensive comparative performance evaluation.
Year
DOI
Venue
2015
10.1016/j.patcog.2014.10.011
Pattern Recognition
Keywords
Field
DocType
Lane model,Lane detection,Lane tracking,Particle filter,Performance evaluation
Polygon,Smooth curves,Pattern recognition,Simulation,Particle filter,Algorithm,Local regression,Lane detection,Artificial intelligence,Detector,Mathematics
Journal
Volume
Issue
ISSN
48
11
0031-3203
Citations 
PageRank 
References 
10
0.66
20
Authors
3
Name
Order
Citations
PageRank
Bok-Suk Shin1689.27
Junli Tao2223.27
Reinhard Klette31743228.94