A Learning Approach Towards Detection and Tracking of Lane Markings

TitleA Learning Approach Towards Detection and Tracking of Lane Markings
Publication TypeJournal Articles
Year of Publication2010
AuthorsGopalan R, Hong T, Shneier M, Chellappa R
JournalIEEE Transactions on Intelligent Transportation Systems
Pagination1 - 12
Date Published2010/02/17/
ISBN Number1524-9050
KeywordsBoosting, Context, Context modeling, Feature extraction, lane marking detection, outlier robustness, Roads, tracking and learning, Training, Vehicles

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 the following: 1) a pixel-hierarchy feature descriptor to model the contextual information shared by lane markings with the surrounding road region; 2) a robust boosting algorithm to select relevant contextual features for detecting lane markings; and 3) particle filters to track the lane markings, without 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.