3D Part-Based Sparse Tracker with Automatic Synchronization and Registration

Handle URI:
http://hdl.handle.net/10754/622773
Title:
3D Part-Based Sparse Tracker with Automatic Synchronization and Registration
Authors:
Bibi, Adel Aamer ( 0000-0002-6169-3918 ) ; Zhang, Tianzhu; Ghanem, Bernard ( 0000-0002-5534-587X )
Abstract:
In this paper, we present a part-based sparse tracker in a particle filter framework where both the motion and appearance model are formulated in 3D. The motion model is adaptive and directed according to a simple yet powerful occlusion handling paradigm, which is intrinsically fused in the motion model. Also, since 3D trackers are sensitive to synchronization and registration noise in the RGB and depth streams, we propose automated methods to solve these two issues. Extensive experiments are conducted on a popular RGBD tracking benchmark, which demonstrate that our tracker can achieve superior results, outperforming many other recent and state-of-the-art RGBD trackers.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Electrical Engineering Program; Visual Computing Center (VCC)
Citation:
Bibi A, Zhang T, Ghanem B (2016) 3D Part-Based Sparse Tracker with Automatic Synchronization and Registration. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Available: http://dx.doi.org/10.1109/CVPR.2016.160.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Issue Date:
13-Dec-2016
DOI:
10.1109/CVPR.2016.160
Type:
Conference Paper
Sponsors:
Research in this publication was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research.
Additional Links:
http://ieeexplore.ieee.org/document/7780529/
Appears in Collections:
Conference Papers; Electrical Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorBibi, Adel Aameren
dc.contributor.authorZhang, Tianzhuen
dc.contributor.authorGhanem, Bernarden
dc.date.accessioned2017-01-29T13:51:38Z-
dc.date.available2017-01-29T13:51:38Z-
dc.date.issued2016-12-13en
dc.identifier.citationBibi A, Zhang T, Ghanem B (2016) 3D Part-Based Sparse Tracker with Automatic Synchronization and Registration. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Available: http://dx.doi.org/10.1109/CVPR.2016.160.en
dc.identifier.doi10.1109/CVPR.2016.160en
dc.identifier.urihttp://hdl.handle.net/10754/622773-
dc.description.abstractIn this paper, we present a part-based sparse tracker in a particle filter framework where both the motion and appearance model are formulated in 3D. The motion model is adaptive and directed according to a simple yet powerful occlusion handling paradigm, which is intrinsically fused in the motion model. Also, since 3D trackers are sensitive to synchronization and registration noise in the RGB and depth streams, we propose automated methods to solve these two issues. Extensive experiments are conducted on a popular RGBD tracking benchmark, which demonstrate that our tracker can achieve superior results, outperforming many other recent and state-of-the-art RGBD trackers.en
dc.description.sponsorshipResearch in this publication was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urlhttp://ieeexplore.ieee.org/document/7780529/en
dc.subjectcomputer graphicsen
dc.subjectimage colour analysisen
dc.subjectimage filteringen
dc.subjectimage registrationen
dc.subjectAdaptation modelsen
dc.subjectBenchmark testingen
dc.subjectSolid modelingen
dc.subjectSynchronizationen
dc.subjectTarget trackingen
dc.subjectThree-dimensional displaysen
dc.title3D Part-Based Sparse Tracker with Automatic Synchronization and Registrationen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentElectrical Engineering Programen
dc.contributor.departmentVisual Computing Center (VCC)en
dc.identifier.journal2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)en
kaust.authorBibi, Adel Aameren
kaust.authorZhang, Tianzhuen
kaust.authorGhanem, Bernarden
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