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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

  • DOI - Digital Object Identifier

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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    JD - Use of computers, robotics and its application

  • OECD FORD branch

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

  • e-ISSN

  • 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