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Robust Visual Vocabulary Tracking Using Hierarchical Model Fusion

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Bozorgtabar, Behzad; Goecke, Roland

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In this paper, we propose a new visual tracking approach based on the Hierarchical Model Fusion framework, which fuses two different trackers to cope with different tracking problems. We use an Incremental Multiple Principal Component Analysis tracker as

dc.contributor.authorBozorgtabar, Behzad
dc.contributor.authorGoecke, Roland
dc.coverage.spatialHobart Australia
dc.date.accessioned2015-12-10T23:22:22Z
dc.date.createdNovember 26-28 2013
dc.identifier.isbn9781479921263
dc.identifier.urihttp://hdl.handle.net/1885/66489
dc.description.abstractIn this paper, we propose a new visual tracking approach based on the Hierarchical Model Fusion framework, which fuses two different trackers to cope with different tracking problems. We use an Incremental Multiple Principal Component Analysis tracker as
dc.publisherConference Organising Committee
dc.relation.ispartofseries2013 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2013
dc.source2013 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2013
dc.titleRobust Visual Vocabulary Tracking Using Hierarchical Model Fusion
dc.typeConference paper
local.description.notesImported from ARIES
local.description.refereedYes
dc.date.issued2013
local.identifier.absfor080104 - Computer Vision
local.identifier.ariespublicationu4334215xPUB1292
local.type.statusPublished Version
local.contributor.affiliationBozorgtabar, Behzad, University of Canberra
local.contributor.affiliationGoecke, Roland, College of Engineering and Computer Science, ANU
local.description.embargo2037-12-31
local.bibliographicCitation.startpage1
local.bibliographicCitation.lastpage8
local.identifier.doi10.1109/DICTA.2013.6691525
local.identifier.absseo970108 - Expanding Knowledge in the Information and Computing Sciences
dc.date.updated2015-12-10T10:30:10Z
local.identifier.scopusID2-s2.0-84893225162
CollectionsANU Research Publications

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