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dc.contributor.authorHamdi, Abdullah
dc.contributor.authorGhanem, Bernard
dc.date.accessioned2017-12-28T07:32:13Z
dc.date.available2017-12-28T07:32:13Z
dc.date.issued2017-08-11
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.
dc.publisherarXiv
dc.relation.urlhttp://arxiv.org/abs/1708.03698v1
dc.relation.urlhttp://arxiv.org/pdf/1708.03698v1
dc.rightsArchived with thanks to arXiv
dc.titleLearning Rotation for Kernel Correlation Filter
dc.typePreprint
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.contributor.departmentVisual Computing Center (VCC)
dc.eprint.versionPre-print
dc.identifier.arxivid1708.03698
kaust.personHamdi, Abdullah
kaust.personGhanem, Bernard
dc.versionv1
refterms.dateFOA2018-06-14T05:29:38Z


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