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dc.contributor.authorDarrell, T.en_US
dc.contributor.authorDemirdjian, D.en_US
dc.contributor.authorChecka, N.en_US
dc.contributor.authorFelzenswalb, P.en_US
dc.date.accessioned2004-10-04T14:37:37Z
dc.date.available2004-10-04T14:37:37Z
dc.date.issued2001-02-01en_US
dc.identifier.otherAIM-2001-001en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/6075
dc.description.abstractIn a known environment, objects may be tracked in multiple views using a set of back-ground models. Stereo-based models can be illumination-invariant, but often have undefined values which inevitably lead to foreground classification errors. We derive dense stereo models for object tracking using long-term, extended dynamic-range imagery, and by detecting and interpolating uniform but unoccluded planar regions. Foreground points are detected quickly in new images using pruned disparity search. We adopt a 'late-segmentation' strategy, using an integrated plan-view density representation. Foreground points are segmented into object regions only when a trajectory is finally estimated, using a dynamic programming-based method. Object entry and exit are optimally determined and are not restricted to special spatial zones.en_US
dc.format.extent5522496 bytes
dc.format.extent672260 bytes
dc.format.mimetypeapplication/postscript
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.relation.ispartofseriesAIM-2001-001en_US
dc.titlePlan-view Trajectory Estimation with Dense Stereo Background Modelsen_US


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