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		<citationkey>PaulaJung:2013:ReDeCl</citationkey>
		<title>Real-time detection and classification of road lane markings</title>
		<format>On-line.</format>
		<year>2013</year>
		<date>Aug. 5-8, 2013</date>
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		<author>Paula, Maurício Braga de,</author>
		<author>Jung, Claudio Rosito,</author>
		<affiliation>Institute of Informatics - Federal University of Rio Grande do Sul and Mathematics and Statistics Department - Federal University of Pelotas</affiliation>
		<affiliation>Institute of Informatics - Federal University of Rio Grande do Sul</affiliation>
		<editor>Boyer, Kim,</editor>
		<editor>Hirata, Nina,</editor>
		<editor>Nedel, Luciana,</editor>
		<editor>Silva, Claudio,</editor>
		<e-mailaddress>mbpaula@inf.ufrgs.br</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 26 (SIBGRAPI)</conferencename>
		<conferencelocation>Arequipa, Peru</conferencelocation>
		<booktitle>Proceedings</booktitle>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<transferableflag>1</transferableflag>
		<contenttype>External Contribution</contenttype>
		<tertiarytype>Full Paper</tertiarytype>
		<keywords>lane detection, lane markings, onboard vehicular cameras, driver assistance system.</keywords>
		<abstract>This paper presents a method for detection and recognition of road lane markings using an uncalibrated onboard camera. Initially, lane boundaries are detected based on a linear- parabolic model. Then, we build a simple model to represent pixels related to the pavement, and explore this model to estimate pixels related to lane markings. A set of features is computed based on the detected lane markings, and a cascade of binary classifiers is adopted to distinguish five types of markings: dashed, dashed-solid, solid-dashed, single-solid and double-solid. Experimental results show that the proposed method presents good classification results under a variety of situations (shadows, varying illumination, etc.).</abstract>
		<language>en</language>
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