Feature enhancement for mean-Shift based object tracking

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dc.contributor.authorGamage, DS
dc.contributor.authorSamarakoon, B
dc.contributor.authorDabarera, R
dc.contributor.authorHandagala, SM
dc.contributor.authorRodrigo, BKRP
dc.date.accessioned2016-08-29T09:11:36Z
dc.date.available2016-08-29T09:11:36Z
dc.date.issued2016-08-29
dc.description.abstractIn object tracking identifying the best feature which discriminates object and background improves the performance. Most of the existing methods do not consider the suitability of such features for the tracker. Here we enhance the discriminative features which elevate the tracker performance. To accommodate object and background variations over time we dynamically update the best feature using a distance measure. We demonstrate the performance of the resulting systems on the UNIVERSITATKARLSRUHE Image Sequences.en_US
dc.identifier.conference5th IEEE International Conference on Information and Automation for Sustainability (ICIAFs) 2010en_US
dc.identifier.departmentDepartment of Electronic and Telecommunication Engineeringen_US
dc.identifier.emailranga@ent.mrt.ac.lken_US
dc.identifier.facultyEngineeringen_US
dc.identifier.pgnospp. 282 - 285en_US
dc.identifier.placeColomboen_US
dc.identifier.urihttp://dl.lib.mrt.ac.lk/handle/123/11968
dc.identifier.year2010en_US
dc.language.isoenen_US
dc.relation.urihttp://dx.doi.org/10.1109/ICIAFS.2010.5715674en_US
dc.source.urihttp://ieeexplore.ieee.org/document/5715674/?arnumber=5715674en_US
dc.subjectTerms-Object tracking, feature enhancement, dynamic update, sigmoid imageen_US
dc.titleFeature enhancement for mean-Shift based object trackingen_US
dc.typeConference-Abstracten_US

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