Learning Rotation for Kernel Correlation Filter

Handle URI:
http://hdl.handle.net/10754/626508
Title:
Learning Rotation for Kernel Correlation Filter
Authors:
Hamdi, Abdullah; Ghanem, Bernard ( 0000-0002-5534-587X )
Abstract:
Kernel Correlation Filters have shown a very promising scheme for visual tracking in terms of speed and accuracy on several benchmarks. However it suffers from problems that affect its performance like occlusion, rotation and scale change. This paper tries to tackle the problem of rotation by reformulating the optimization problem for learning the correlation filter. This modification (RKCF) includes learning rotation filter that utilizes circulant structure of HOG feature to guesstimate rotation from one frame to another and enhance the detection of KCF. Hence it gains boost in overall accuracy in many of OBT50 detest videos with minimal additional computation.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Electrical Engineering Program; Visual Computing Center (VCC)
Publisher:
arXiv
Issue Date:
11-Aug-2017
ARXIV:
arXiv:1708.03698
Type:
Preprint
Additional Links:
http://arxiv.org/abs/1708.03698v1; http://arxiv.org/pdf/1708.03698v1
Appears in Collections:
Other/General Submission; Other/General Submission; Electrical Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorHamdi, Abdullahen
dc.contributor.authorGhanem, Bernarden
dc.date.accessioned2017-12-28T07:32:13Z-
dc.date.available2017-12-28T07:32:13Z-
dc.date.issued2017-08-11en
dc.identifier.urihttp://hdl.handle.net/10754/626508-
dc.description.abstractKernel Correlation Filters have shown a very promising scheme for visual tracking in terms of speed and accuracy on several benchmarks. However it suffers from problems that affect its performance like occlusion, rotation and scale change. This paper tries to tackle the problem of rotation by reformulating the optimization problem for learning the correlation filter. This modification (RKCF) includes learning rotation filter that utilizes circulant structure of HOG feature to guesstimate rotation from one frame to another and enhance the detection of KCF. Hence it gains boost in overall accuracy in many of OBT50 detest videos with minimal additional computation.en
dc.publisherarXiven
dc.relation.urlhttp://arxiv.org/abs/1708.03698v1en
dc.relation.urlhttp://arxiv.org/pdf/1708.03698v1en
dc.rightsArchived with thanks to arXiven
dc.titleLearning Rotation for Kernel Correlation Filteren
dc.typePreprinten
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentElectrical Engineering Programen
dc.contributor.departmentVisual Computing Center (VCC)en
dc.eprint.versionPre-printen
dc.identifier.arxividarXiv:1708.03698en
kaust.authorHamdi, Abdullahen
kaust.authorGhanem, Bernarden
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.