Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/70324
Citations
Scopus Web of Science® Altmetric
?
?
Full metadata record
DC FieldValueLanguage
dc.contributor.authorLuo, Wenhanen
dc.contributor.authorLi, Xien
dc.contributor.authorLi, Weien
dc.contributor.authorHu, Weimingen
dc.date.issued2011en
dc.identifier.citationProceedings of the 2011 18th IEEE International Conference on Image Processing, 2011: pp.485-488en
dc.identifier.isbn9781457713033en
dc.identifier.urihttp://hdl.handle.net/2440/70324-
dc.description.abstractIn this paper, we propose a boosting based tracking framework using transfer learning. To deal with complex appearance variations, the proposed tracking framework tries to utilize discriminative information from previous frames to conduct the tracking task in the current frame, and thus transfers some prior knowledge from the previous source data domain to the current target data domain, resulting in a high discriminative tracker for distinguishing the object from the background. The proposed tracking system has been tested on several challenging sequences. Experimental results demonstrate the effectiveness of the proposed tracking framework.en
dc.description.statementofresponsibilityWenhan Luo, Xi Li, Wei Li, Weiming Huen
dc.language.isoenen
dc.publisherIEEEen
dc.rightsCopyright © 2011 by IEEE.en
dc.subjecttracking; transfer learning; boostingen
dc.titleRobust visual tracking via transfer learningen
dc.typeConference paperen
dc.contributor.schoolSchool of Computer Scienceen
dc.contributor.conferenceIEEE International Conference on Image Processing (18th : 2011 : Brussels, Belgium)en
dc.contributor.conferenceICIP 2011en
dc.identifier.doi10.1109/ICIP.2011.6116557en
Appears in Collections:Computer Science publications

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.