@techreport{TD:100768,
	att_abstract={{Road scene analysis is a challenging problem that
has applications in autonomous navigation of vehicles. An integral
component of this system is the robust detection and tracking
of lane markings. It is a hard problem primarily due to large
appearance variations in lane markings caused by factors such as
occlusion (traffic on the road), shadows (from objects like trees),
and changing lighting conditions of the scene (transition from
day to night). In this paper, we address these issues through
a learning-based approach using visual inputs from a camera
mounted in front of a vehicle. We propose, (i) a pixel-hierarchy
feature descriptor to model contextual information shared by lane
markings with the surrounding road region, (ii) a robust boosting
algorithm to select relevant contextual features for detecting lane
markings, and (iii) particle filters to track the lane markings,
without the knowledge of vehicle speed, by assuming the lane
markings to be static through the video sequence and then
learning the possible road scene variations from the statistics
of tracked model parameters. We investigate the effectiveness of
our algorithm on challenging daylight and night-time road video
sequences.}},
	att_authors={rg4675},
	att_categories={},
	att_copyright={{IEEE}},
	att_copyright_notice={{This version of the work is reprinted here with permission of IEEE for your personal use. Not for redistribution. The definitive version was published in 2012. {{, 2012-03-31}}
This version of the work is reprinted here with permission of IEEE for your personal use. Not for redistribution. The definitive version was published in 2012. {{, 2012-03-31}}
}},
	att_donotupload={},
	att_private={false},
	att_projects={},
	att_tags={Lane Marking Detection,  Context,  Boosting,  Outlier Robustness,  Tracking and Learning},
	att_techdoc={true},
	att_techdoc_key={TD:100768},
	att_url={http://web1.research.att.com:81/techdocs_downloads/TD:100768_DS1_2012-01-16T17:04:00.212Z.pdf},
	author={Raghuraman Gopalan and Tsai Hong and Michael Shneier and Rama Chellappa},
	institution={{IEEE Transactions on Intelligent Transportation Systems}},
	month={March},
	title={{A Learning Approach Towards Detection and
Tracking of Lane Markings}},
	year=2012,
}