Video-based Lane Detection using Boosting PrinciplesRaghuraman Gopalan1, Tsai Hong2, Mike Shneier2 and Rama Chellappa1

1Center for Automation Research, University of Maryland, College Park, MD, 207422National Institute of Standards and Technology, Gaithersburg, MD, 20899

Sample Lane Detection ResultsFormulation of the detection problem

Learning prior information abouttraining sample weights

•Localizing the lane markings (from the Adaboost results) using color, gradient magnitude, and texture cues•Removing potential false alarms using scene-specific information such as, spacing between two lanes.•Verifying the continuity of lane markings using gradient orientation, which is robust against lighting variations.

Post-processing

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We formulate a boosting-based framework for detecting road lane markings in daylight and night-time videos. Specifically, we study a set of reliable features that help perform robust detection under the presence of shadows. This forms an integral component in road scene analysis

Introduction

•Distinct visual appearance cues when compared with other regions in the scene•Presence of strong gradient magnitude patterns •Once localized, enables verification using gradient direction that offers good invariance to lighting variations

•Boosting algorithms: For the set of two-class problems, they learn a linear classification function by combining several weak classifiers•Why Real Adaboost ? Allows a range of classification intervals for each weak classifier to perform classification, rather than having a single threshold

Real Adaboost

Experimental Results

References[1] J.C. McCall, and M.M. Trivedi. Video-based lane estimation and tracking: survey, IEEE Transactions on Intelligent Transportation Systems, 7(1), 2006.[2] R.E. Schapire, and Y. Singer. Improved boosting algorithms using confidence-rated predictions. Machine Learning, 37(3), 1999.[3] E.D. Dickmanns. Dynamic vision for perception and control of motion. Springer, 2007.

Features of Lane markings

(a): discrete threshold, (b) real valued

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•Two Real Adaboost chains•Appearance – Haar-like weak classifiers•Edge patterns – set of lines, and curves (of different slopes and curvatures)

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•Normally, all training samples are initialized with same weights before performing boosting.•What is the drawback? Some training data may be evidently more representative of the class than others•Learning this prior information using Kernel Discriminant Analysis

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