<?xml version="1.0" encoding="ISO-8859-1"?>
<metadatalist>
	<metadata ReferenceType="Conference Proceedings">
		<site>sibgrapi.sid.inpe.br 802</site>
		<holdercode>{ibi 8JMKD3MGPEW34M/46T9EHH}</holdercode>
		<identifier>8JMKD3MGPBW34M/3EDKHJL</identifier>
		<repository>sid.inpe.br/sibgrapi/2013/07.05.14.27</repository>
		<lastupdate>2013:07.05.14.27.43 sid.inpe.br/banon/2001/03.30.15.38 administrator</lastupdate>
		<metadatarepository>sid.inpe.br/sibgrapi/2013/07.05.14.27.43</metadatarepository>
		<metadatalastupdate>2022:06.14.00.07.44 sid.inpe.br/banon/2001/03.30.15.38 administrator {D 2013}</metadatalastupdate>
		<doi>10.1109/SIBGRAPI.2013.21</doi>
		<citationkey>PaulaJung:2013:ReDeCl</citationkey>
		<title>Real-time detection and classification of road lane markings</title>
		<format>On-line.</format>
		<year>2013</year>
		<numberoffiles>1</numberoffiles>
		<size>1046 KiB</size>
		<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>
		<date>5-8 Aug. 2013</date>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Full Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<versiontype>finaldraft</versiontype>
		<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>
		<targetfile>114712_camera_ready-PID2846723.pdf</targetfile>
		<usergroup>mbpaula@inf.ufrgs.br</usergroup>
		<visibility>shown</visibility>
		<mirrorrepository>sid.inpe.br/banon/2001/03.30.15.38.24</mirrorrepository>
		<nexthigherunit>8JMKD3MGPEW34M/46SLB4P</nexthigherunit>
		<nexthigherunit>8JMKD3MGPEW34M/4742MCS</nexthigherunit>
		<citingitemlist>sid.inpe.br/sibgrapi/2022/05.15.04.02 7</citingitemlist>
		<hostcollection>sid.inpe.br/banon/2001/03.30.15.38</hostcollection>
		<agreement>agreement.html .htaccess .htaccess2</agreement>
		<lasthostcollection>sid.inpe.br/banon/2001/03.30.15.38</lasthostcollection>
		<url>http://sibgrapi.sid.inpe.br/rep-/sid.inpe.br/sibgrapi/2013/07.05.14.27</url>
	</metadata>
</metadatalist>