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Article: Lane detection by orientation and length discrimination

TitleLane detection by orientation and length discrimination
Authors
Issue Date2000
PublisherIEEE.
Citation
Ieee Transactions On Systems, Man, And Cybernetics, Part B: Cybernetics, 2000, v. 30 n. 4, p. 539-548 How to Cite?
AbstractThis paper describes a novel lane detection algorithm for visual traffic surveillance applications under the auspice of intelligent transportation systems. Traditional lane detection methods for vehicle navigation typically use spatial masks to isolate instantaneous lane information from on-vehicle camera images. When surveillance is concerned, complete lane and multiple lane information is essential for tracking vehicles and monitoring lane change frequency from overhead cameras, where traditional methods become inadequate. The algorithm presented in this paper extracts complete multiple lane information by utilizing prominent orientation and length features of lane markings and curb structures to discriminate against other minor features. Essentially, edges are first extracted from the background of a traffic sequence, then thinned and approximated by straight lines. From the resulting set of straight lines, orientation and length discriminations are carried out three-dimensionally with the aid of two-dimensional (2-D) to three-dimensional (3-D) coordinate transformation and K-means clustering. By doing so, edges with strong orientation and length affinity are retained and clustered, while short and isolated edges are eliminated. Overall, the merits of this algorithm are as follows. First, it works well under practical visual surveillance conditions. Second, using K-means for clustering offers a robust approach. Third, the algorithm is efficient as it only requires one image frame to determine the road center lines. Fourth, it computes multiple lane information simultaneously. Fifth, the center lines determined are accurate enough for the intended application.
Persistent Identifierhttp://hdl.handle.net/10722/42870
ISSN
2014 Impact Factor: 6.22
2015 SCImago Journal Rankings: 3.921
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLai, AHSen_HK
dc.contributor.authorYung, NHCen_HK
dc.date.accessioned2007-03-23T04:33:45Z-
dc.date.available2007-03-23T04:33:45Z-
dc.date.issued2000en_HK
dc.identifier.citationIeee Transactions On Systems, Man, And Cybernetics, Part B: Cybernetics, 2000, v. 30 n. 4, p. 539-548en_HK
dc.identifier.issn1083-4419en_HK
dc.identifier.urihttp://hdl.handle.net/10722/42870-
dc.description.abstractThis paper describes a novel lane detection algorithm for visual traffic surveillance applications under the auspice of intelligent transportation systems. Traditional lane detection methods for vehicle navigation typically use spatial masks to isolate instantaneous lane information from on-vehicle camera images. When surveillance is concerned, complete lane and multiple lane information is essential for tracking vehicles and monitoring lane change frequency from overhead cameras, where traditional methods become inadequate. The algorithm presented in this paper extracts complete multiple lane information by utilizing prominent orientation and length features of lane markings and curb structures to discriminate against other minor features. Essentially, edges are first extracted from the background of a traffic sequence, then thinned and approximated by straight lines. From the resulting set of straight lines, orientation and length discriminations are carried out three-dimensionally with the aid of two-dimensional (2-D) to three-dimensional (3-D) coordinate transformation and K-means clustering. By doing so, edges with strong orientation and length affinity are retained and clustered, while short and isolated edges are eliminated. Overall, the merits of this algorithm are as follows. First, it works well under practical visual surveillance conditions. Second, using K-means for clustering offers a robust approach. Third, the algorithm is efficient as it only requires one image frame to determine the road center lines. Fourth, it computes multiple lane information simultaneously. Fifth, the center lines determined are accurate enough for the intended application.en_HK
dc.format.extent774872 bytes-
dc.format.extent5183 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cyberneticsen_HK
dc.rights©2000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleLane detection by orientation and length discriminationen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1083-4419&volume=30&issue=4&spage=539&epage=548&date=2000&atitle=Lane+detection+by+orientation+and+length+discriminationen_HK
dc.identifier.emailYung, NHC:nyung@eee.hku.hken_HK
dc.identifier.authorityYung, NHC=rp00226en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/3477.865171en_HK
dc.identifier.scopuseid_2-s2.0-0034247777en_HK
dc.identifier.hkuros59360-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0034247777&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume30en_HK
dc.identifier.issue4en_HK
dc.identifier.spage539en_HK
dc.identifier.epage548en_HK
dc.identifier.isiWOS:000089118000005-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridLai, AHS=7102225794en_HK
dc.identifier.scopusauthoridYung, NHC=7003473369en_HK

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