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dc.contributor.authorZhang, Tianzhu
dc.contributor.authorGhanem, Bernard
dc.contributor.authorXu, Changsheng
dc.contributor.authorAhuja, Narendra
dc.date.accessioned2015-08-04T07:14:12Z
dc.date.available2015-08-04T07:14:12Z
dc.date.issued2013-06
dc.identifier.isbn9780769549903
dc.identifier.issn21607508
dc.identifier.doi10.1109/CVPRW.2013.150
dc.identifier.urihttp://hdl.handle.net/10754/564735
dc.description.abstractSparse representation based methods have recently drawn much attention in visual tracking due to good performance against illumination variation and occlusion. They assume the errors caused by image variations can be modeled as pixel-wise sparse. However, in many practical scenarios these errors are not truly pixel-wise sparse but rather sparsely distributed in a structured way. In fact, pixels in error constitute contiguous regions within the object's track. This is the case when significant occlusion occurs. To accommodate for non-sparse occlusion in a given frame, we assume that occlusion detected in previous frames can be propagated to the current one. This propagated information determines which pixels will contribute to the sparse representation of the current track. In other words, pixels that were detected as part of an occlusion in the previous frame will be removed from the target representation process. As such, this paper proposes a novel tracking algorithm that models and detects occlusion through structured sparse learning. We test our tracker on challenging benchmark sequences, such as sports videos, which involve heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that our tracker consistently outperforms the state-of-the-art. © 2013 IEEE.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.titleObject tracking by occlusion detection via structured sparse learning
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.contributor.departmentVisual Computing Center (VCC)
dc.contributor.departmentVCC Analytics Research Group
dc.identifier.journal2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops
dc.conference.date23 June 2013 through 28 June 2013
dc.conference.name2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2013
dc.conference.locationPortland, OR
dc.contributor.institutionAdvanced Digital Sciences Center of Illinois, Singapore
dc.contributor.institutionInstitute of Automation, Chinese Academy of Sciences, China
dc.contributor.institutionUniversity of Illinois at Urbana-Champaign, Urbana, IL, United States
kaust.personGhanem, Bernard


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