Feature enhancement for mean-Shift based object tracking

Gamage, DS; Samarakoon, B; Dabarera, R; Handagala, SM; Rodrigo, BKRP

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dc.contributor.author	Gamage, DS
dc.contributor.author	Samarakoon, B
dc.contributor.author	Dabarera, R
dc.contributor.author	Handagala, SM
dc.contributor.author	Rodrigo, BKRP
dc.date.accessioned	2016-08-29T09:11:36Z
dc.date.available	2016-08-29T09:11:36Z
dc.date.issued	2016-08-29
dc.identifier.uri	http://dl.lib.mrt.ac.lk/handle/123/11968
dc.description.abstract	In 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.language.iso	en	en_US
dc.relation.uri	http://dx.doi.org/10.1109/ICIAFS.2010.5715674	en_US
dc.source.uri	http://ieeexplore.ieee.org/document/5715674/?arnumber=5715674	en_US
dc.subject	Terms-Object tracking, feature enhancement, dynamic update, sigmoid image	en_US
dc.title	Feature enhancement for mean-Shift based object tracking	en_US
dc.type	Conference-Abstract	en_US
dc.identifier.faculty	Engineering	en_US
dc.identifier.department	Department of Electronic and Telecommunication Engineering	en_US
dc.identifier.year	2010	en_US
dc.identifier.conference	5th IEEE International Conference on Information and Automation for Sustainability (ICIAFs) 2010	en_US
dc.identifier.place	Colombo	en_US
dc.identifier.pgnos	pp. 282 - 285	en_US
dc.identifier.email	ranga@ent.mrt.ac.lk	en_US