@article {12493,
	title = {A Learning Approach Towards Detection and Tracking of Lane Markings},
	journal = {IEEE Transactions on Intelligent Transportation Systems},
	volume = {PP},
	year = {2010},
	month = {2010/02/17/},
	pages = {1 - 12},
	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 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.},
	keywords = {Boosting, Context, Context modeling, Feature extraction, lane marking detection, outlier robustness, Roads, tracking and learning, Training, Vehicles},
	isbn = {1524-9050},
	doi = {10.1109/TITS.2012.2184756},
	author = {Gopalan,R. and Hong, T. and Shneier, M. and Chellapa, Rama}
}
@conference {17660,
	title = {Splitters and near-optimal derandomization},
	booktitle = {, 36th Annual Symposium on Foundations of Computer Science, 1995. Proceedings},
	year = {1995},
	month = {1995/10/23/25},
	pages = {182 - 191},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We present a fairly general method for finding deterministic constructions obeying what we call k-restrictions; this yields structures of size not much larger than the probabilistic bound. The structures constructed by our method include (n,k)-universal sets (a collection of binary vectors of length n such that for any subset of size k of the indices, all 2k configurations appear) and families of perfect hash functions. The near-optimal constructions of these objects imply the very efficient derandomization of algorithms in learning, of fixed-subgraph finding algorithms, and of near optimal ΣIIΣ threshold formulae. In addition, they derandomize the reduction showing the hardness of approximation of set cover. They also yield deterministic constructions for a local-coloring protocol, and for exhaustive testing of circuits},
	keywords = {Boosting, Circuit testing, computational complexity, computational linguistics, Computer science, Contracts, derandomization, deterministic constructions, Educational institutions, Engineering profession, exhaustive testing, fairly general method, fixed-subgraph finding algorithms, hardness of approximation, Information systems, k-restrictions, learning, local-coloring protocol, MATHEMATICS, near-optimal constructions, near-optimal derandomization, Parallel algorithms, probabilistic bound, probability, Protocols, randomised algorithms, Set cover, splitters},
	isbn = {0-8186-7183-1},
	doi = {10.1109/SFCS.1995.492475},
	author = {Naor,M. and Schulman,L. J and Srinivasan, Aravind}
}
