Tracking with Context as a Semi-supervised Learning and Labeling Problem
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F12%3A00200377" target="_blank" >RIV/68407700:21230/12:00200377 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Tracking with Context as a Semi-supervised Learning and Labeling Problem
Original language description
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
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
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Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
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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Number of pages
4
Pages from-to
2124-2127
Publisher name
IEEE
Place of publication
New York
Event location
Tsukuba
Event date
Nov 11, 2012
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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