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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F12%3A00200611%21RIV13-GA0-21230___
rdf:type
skos:Concept n13:Vysledek
dcterms:description
The paper presents contributions to the design of the Flock of Trackers (FoT). The FoT trackers estimate the pose of the tracked object by robustly combining displacement estimates from a subset of predicted local trackers that cover the object. The first contribution, called the Cell FoT, allows local trackers to drift to points good to track. The Cell FoT was compared with the Kalal et al. Grid FoT and outperformed it on all sequences but one and for all local failure prediction methods. As a second contribution, we introduce two new predictors of local tracker failure - the neighbourhood consistency predictor (Nh) and the Markov predictor (Mp) and show that the new predictors combined with the normalized cross-correlation (NCC) predictor are more powerful and almost two times faster than the predictor based on normalized cross-correlation (NCC) and forward-backward procedure (FB). The resulting tracker equipped with the new predictors combined with the normalized cross-correlation predictor was compared with state-of-the-art tracking algorithms and surpassed them in terms of the number of sequences where a given tracking algorithm performed best. The paper presents contributions to the design of the Flock of Trackers (FoT). The FoT trackers estimate the pose of the tracked object by robustly combining displacement estimates from a subset of predicted local trackers that cover the object. The first contribution, called the Cell FoT, allows local trackers to drift to points good to track. The Cell FoT was compared with the Kalal et al. Grid FoT and outperformed it on all sequences but one and for all local failure prediction methods. As a second contribution, we introduce two new predictors of local tracker failure - the neighbourhood consistency predictor (Nh) and the Markov predictor (Mp) and show that the new predictors combined with the normalized cross-correlation (NCC) predictor are more powerful and almost two times faster than the predictor based on normalized cross-correlation (NCC) and forward-backward procedure (FB). The resulting tracker equipped with the new predictors combined with the normalized cross-correlation predictor was compared with state-of-the-art tracking algorithms and surpassed them in terms of the number of sequences where a given tracking algorithm performed best.
dcterms:title
Increasing Robustness of the Flock of Trackers Increasing Robustness of the Flock of Trackers
skos:prefLabel
Increasing Robustness of the Flock of Trackers Increasing Robustness of the Flock of Trackers
skos:notation
RIV/68407700:21230/12:00200611!RIV13-GA0-21230___
n13:predkladatel
n14:orjk%3A21230
n3:aktivita
n5:P
n3:aktivity
P(GBP103/12/G084)
n3:dodaniDat
n8:2013
n3:domaciTvurceVysledku
n16:1711326 n16:2511169
n3:druhVysledku
n17:O
n3:duvernostUdaju
n15:S
n3:entitaPredkladatele
n4:predkladatel
n3:idSjednocenehoVysledku
141127
n3:idVysledku
RIV/68407700:21230/12:00200611
n3:jazykVysledku
n12:eng
n3:klicovaSlova
tracking; predictors; flock
n3:klicoveSlovo
n7:tracking n7:flock n7:predictors
n3:kontrolniKodProRIV
[53A0F6BDA7E6]
n3:obor
n11:JD
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:projekt
n6:GBP103%2F12%2FG084
n3:rokUplatneniVysledku
n8:2012
n3:tvurceVysledku
Matas, Jiří Vojíř, Tomáš
n18:organizacniJednotka
21230