Tracking with Context as a Semi-supervised Learning and Labeling Problem

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Tracking with Context as a Semi-supervised Learning and Labeling Problem

Popis výsledku v původním jazyce

It is suggested how a Markov random field can be used for object tracking with context information. The tracking is formulated as a two layer process. In the first phase, the image is represented by a set of feature points which are tracked by a standardtracker. In the second phase, the proposed semi-supervised learning and labeling algorithm is used to label the points to three classes - object, background and companion. The object state (pose) is defined by the set of points labeled as the object. The companion represents the object context and contains non-object points with a motion similar to the motion of the object. As initialization, labels of the object points only are provided by a user in the very first frame. The appearance and motion models of the three classes and the labels of the remaining points in the whole video sequence are estimated in a GrabCut fashion. We show that the use of the companion class together with a 3D (space-time) Markov random field helps to identi

Název v anglickém jazyce

Tracking with Context as a Semi-supervised Learning and Labeling Problem

Popis výsledku anglicky

It is suggested how a Markov random field can be used for object tracking with context information. The tracking is formulated as a two layer process. In the first phase, the image is represented by a set of feature points which are tracked by a standardtracker. In the second phase, the proposed semi-supervised learning and labeling algorithm is used to label the points to three classes - object, background and companion. The object state (pose) is defined by the set of points labeled as the object. The companion represents the object context and contains non-object points with a motion similar to the motion of the object. As initialization, labels of the object points only are provided by a user in the very first frame. The appearance and motion models of the three classes and the labels of the remaining points in the whole video sequence are estimated in a GrabCut fashion. We show that the use of the companion class together with a 3D (space-time) Markov random field helps to identi

Druh

D - Stať ve sborníku

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2012

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

ICPR 2012: Proceedings of 21st International Conference on Pattern Recognition

ISBN

978-4-9906441-0-9

ISSN

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e-ISSN

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Počet stran výsledku

4

Strana od-do

2124-2127

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

Tsukuba

Datum konání akce

11. 11. 2012

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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