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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F13%3A00212540%21RIV14-TA0-21230___
rdf:type
skos:Concept n15:Vysledek
dcterms:description
Mean-Shift tracking is a popular algorithm for object tracking since it is easy to implement and it is fast and robust. In this paper, we address the problem of scale adaptation of the Hellinger distance based Mean-Shift tracker. We start from a theoretical derivation of scale estimation in the Mean-Shift framework. To make the scale estimation robust and suitable for tracking, we in- troduce regularization terms that counter two major problem: (i) scale expansion caused by background clutter and (ii) scale implosion on self-similar objects. To further robustify the scale estimate, it is validated by a forward-backward consis- tency check. The proposed Mean-shift tracker with scale selection is compared with re- cent state-of-the-art algorithms on a dataset of 48 public color sequences and it achieved excellent results. Mean-Shift tracking is a popular algorithm for object tracking since it is easy to implement and it is fast and robust. In this paper, we address the problem of scale adaptation of the Hellinger distance based Mean-Shift tracker. We start from a theoretical derivation of scale estimation in the Mean-Shift framework. To make the scale estimation robust and suitable for tracking, we in- troduce regularization terms that counter two major problem: (i) scale expansion caused by background clutter and (ii) scale implosion on self-similar objects. To further robustify the scale estimate, it is validated by a forward-backward consis- tency check. The proposed Mean-shift tracker with scale selection is compared with re- cent state-of-the-art algorithms on a dataset of 48 public color sequences and it achieved excellent results.
dcterms:title
Robust Scale-Adaptive Mean-Shift for Tracking Robust Scale-Adaptive Mean-Shift for Tracking
skos:prefLabel
Robust Scale-Adaptive Mean-Shift for Tracking Robust Scale-Adaptive Mean-Shift for Tracking
skos:notation
RIV/68407700:21230/13:00212540!RIV14-TA0-21230___
n15:predkladatel
n16:orjk%3A21230
n3:aktivita
n8:P
n3:aktivity
P(GBP103/12/G084), P(TE01020415)
n3:dodaniDat
n12:2014
n3:domaciTvurceVysledku
n18:1711326 n18:2511169
n3:druhVysledku
n22:D
n3:duvernostUdaju
n13:S
n3:entitaPredkladatele
n20:predkladatel
n3:idSjednocenehoVysledku
103173
n3:idVysledku
RIV/68407700:21230/13:00212540
n3:jazykVysledku
n19:eng
n3:klicovaSlova
object tracking; mean-shift; scale estimation
n3:klicoveSlovo
n5:mean-shift n5:object%20tracking n5:scale%20estimation
n3:kontrolniKodProRIV
[DB127715649A]
n3:mistoKonaniAkce
Espoo
n3:mistoVydani
Heidelberg
n3:nazevZdroje
SCIA 2013: Proceedings of the 18th Scandinavian Conference on Image Analysis
n3:obor
n23:JD
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
3
n3:projekt
n7:TE01020415 n7:GBP103%2F12%2FG084
n3:rokUplatneniVysledku
n12:2013
n3:tvurceVysledku
Matas, Jiří Nosková, Jana Vojíř, Tomáš
n3:typAkce
n10:WRD
n3:zahajeniAkce
2013-06-17+02:00
s:issn
0302-9743
s:numberOfPages
12
n21:doi
10.1007/978-3-642-38886-6_61
n11:hasPublisher
Springer-Verlag
n9:isbn
978-3-642-38885-9
n17:organizacniJednotka
21230