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dc.contributor.author Lim, Young-Chul ko
dc.contributor.author Kang, Minsung ko
dc.date.available 2017-07-11T07:50:14Z -
dc.date.created 2017-06-25 -
dc.date.issued 2014 -
dc.identifier.citation 11th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2014, v.2, pp.788 - 793 -
dc.identifier.isbn 9789897580406 -
dc.identifier.uri http://hdl.handle.net/20.500.11750/3795 -
dc.description.abstract In order to detect vehicles on the road reliably, a vehicle detector and tracker should be integrated to work in unison. In real applications, some of the ROIs generated from a vehicle detector are often ill-fitting due to imperfect detector outputs. The ill-fitting ROIs make it difficult for tracker to estimate a target vehicle correctly due to outliers. In this paper, we propose a stereo-based visual tracking method using a 3D feature clustering scheme to overcome this problem. Our method selects reliable features using feature matching and a 3D feature clustering method and estimates an accurate transform model using a modified RANSAC algorithm. Our experimental results demonstrate that the proposed method offers better performance compared with previous feature-based tracking methods. -
dc.publisher SciTePress -
dc.title Stereo vision-based visual tracking using 3D feature clustering for robust vehicle tracking -
dc.type Conference -
dc.identifier.scopusid 2-s2.0-84910047173 -
dc.type.rims CONF -
dc.identifier.citationVolume 2 -
dc.identifier.citationStartPage 788 -
dc.identifier.citationEndPage 793 -
dc.identifier.citationTitle 11th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2014 -
dc.type.journalArticle Conference Paper -
dc.identifier.conferencecountry PO -
dc.contributor.affiliatedAuthor Lim, Young-Chul -
dc.contributor.affiliatedAuthor Kang, Minsung -
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Convergence Research Center for Future Automotive Technology 2. Conference Papers

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