About: Robust scale-adaptive mean-shift for tracking

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An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

Attributes	Values
rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
rdfs:seeAlso	http://www.sciencedirect.com/science/article/pii/S0167865514001056
Description	The mean-shift procedure is a popular object tracking algorithm since it is f ast, easy to implement and performs well in a range of conditions. We address the problem of s cale adaptation and present a novel theoretically justified scale estimation mechanism which relies solely on the mean-shift procedure for the Hellinger distance. We also propose two impro vements of the mean-shift tracker that make the scale estimation more robust in the presence of background clutter. The first one is a novel histogram color weighting that exploits the object neighborhood to help discriminate the target called background ratio weighting (BRW). We s how that the BRW improves performance of MS-like tracking methods in general. The second impro vement boost the performance of the tracker with the proposed scale estimation by the introduc tion of a forward-backward consistency check and by adopting regularization terms that counter two major problems: scale expansion caused by background clutter and scale implosion on self-similar objects. The proposed mean-shift tracker with scale selection and BRW is compared with recent state-of-the-art algorithms on a dataset of 77 public sequences. It outperforms the re ference algorithms in average recall, processing speed and it achieves the best score for 30% of the sequences - the highest percentage among the reference algorithms. The mean-shift procedure is a popular object tracking algorithm since it is f ast, easy to implement and performs well in a range of conditions. We address the problem of s cale adaptation and present a novel theoretically justified scale estimation mechanism which relies solely on the mean-shift procedure for the Hellinger distance. We also propose two impro vements of the mean-shift tracker that make the scale estimation more robust in the presence of background clutter. The first one is a novel histogram color weighting that exploits the object neighborhood to help discriminate the target called background ratio weighting (BRW). We s how that the BRW improves performance of MS-like tracking methods in general. The second impro vement boost the performance of the tracker with the proposed scale estimation by the introduc tion of a forward-backward consistency check and by adopting regularization terms that counter two major problems: scale expansion caused by background clutter and scale implosion on self-similar objects. The proposed mean-shift tracker with scale selection and BRW is compared with recent state-of-the-art algorithms on a dataset of 77 public sequences. It outperforms the re ference algorithms in average recall, processing speed and it achieves the best score for 30% of the sequences - the highest percentage among the reference algorithms. (en)
Title	Robust scale-adaptive mean-shift for tracking Robust scale-adaptive mean-shift for tracking (en)
skos:prefLabel	Robust scale-adaptive mean-shift for tracking Robust scale-adaptive mean-shift for tracking (en)
skos:notation	RIV/68407700:21230/14:00223270!RIV15-GA0-21230___
http://linked.open...avai/riv/aktivita	P
http://linked.open...avai/riv/aktivity	P(GBP103/12/G084)
http://linked.open...iv/cisloPeriodika	November
http://linked.open...vai/riv/dodaniDat	2015
http://linked.open...aciTvurceVysledku	Matas, Jiří Vojíř, Tomáš
http://linked.open.../riv/druhVysledku	J - Článek v odborném periodiku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	České vysoké učení technické v Praze / Fakulta elektrotechnická
http://linked.open...dnocenehoVysledku	42978
http://linked.open...ai/riv/idVysledku	RIV/68407700:21230/14:00223270
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	tracking; mean-shift (en)
http://linked.open.../riv/klicoveSlovo	tracking mean-shift
http://linked.open...odStatuVydavatele	NL - Nizozemsko
http://linked.open...ontrolniKodProRIV	[215D67A2BB75]
http://linked.open...i/riv/nazevZdroje	Pattern Recognition Letters
http://linked.open...in/vavai/riv/obor	JD
http://linked.open...ichTvurcuVysledku	2 (xsd:int)
http://linked.open...cetTvurcuVysledku	3 (xsd:int)
http://linked.open...vavai/riv/projekt	Center for Large Scale Multi-modal Data Interpretation
http://linked.open...UplatneniVysledku	2014
http://linked.open...v/svazekPeriodika	49
http://linked.open...iv/tvurceVysledku	Matas, Jiří Nosková, Jana Vojíř, Tomáš
http://linked.open...ain/vavai/riv/wos	000343852400034
issn	0167-8655
number of pages	9 (xsd:int)
http://bibframe.org/vocab/doi	10.1016/j.patrec.2014.03.025
http://localhost/t...ganizacniJednotka	21230

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