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dc.contributor.authorYang, Yanchao
dc.contributor.authorSundaramoorthi, Ganesh
dc.date.accessioned2015-08-04T07:17:13Z
dc.date.available2015-08-04T07:17:13Z
dc.date.issued2013-12
dc.identifier.isbn9781479928392
dc.identifier.doi10.1109/ICCV.2013.32
dc.identifier.urihttp://hdl.handle.net/10754/564821
dc.description.abstractWe present a method to track the precise shape of a dynamic object in video. Joint dynamic shape and appearance models, in which a template of the object is propagated to match the object shape and radiance in the next frame, are advantageous over methods employing global image statistics in cases of complex object radiance and cluttered background. In cases of complex 3D object motion and relative viewpoint change, self-occlusions and disocclusions of the object are prominent, and current methods employing joint shape and appearance models are unable to accurately adapt to new shape and appearance information, leading to inaccurate shape detection. In this work, we model self-occlusions and dis-occlusions in a joint shape and appearance tracking framework. Experiments on video exhibiting occlusion/dis-occlusion, complex radiance and background show that occlusion/dis-occlusion modeling leads to superior shape accuracy compared to recent methods employing joint shape/appearance models or employing global statistics. © 2013 IEEE.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectdis-occlusions
dc.subjectlevel set methods
dc.subjectobject tracking
dc.subjectocclusions
dc.subjectoptical flow
dc.subjectshape tracking
dc.titleModeling self-occlusions in dynamic shape and appearance tracking
dc.typeConference Paper
dc.contributor.departmentElectrical Engineering Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentVisual Computing Center (VCC)
dc.identifier.journal2013 IEEE International Conference on Computer Vision
dc.conference.date1 December 2013 through 8 December 2013
dc.conference.name2013 14th IEEE International Conference on Computer Vision, ICCV 2013
dc.conference.locationSydney, NSW
kaust.personYang, Yanchao
kaust.personSundaramoorthi, Ganesh


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