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rdf:type
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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 standard tracker. 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 identify object points behind full occlusions or under strong appearance changes
- 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 standard tracker. 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 identify object points behind full occlusions or under strong appearance changes (en)
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Title
| - Tracking with Context as a Semi-supervised Learning and Labeling Problem
- Tracking with Context as a Semi-supervised Learning and Labeling Problem (en)
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skos:prefLabel
| - Tracking with Context as a Semi-supervised Learning and Labeling Problem
- Tracking with Context as a Semi-supervised Learning and Labeling Problem (en)
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skos:notation
| - RIV/68407700:21230/12:00200377!RIV13-MSM-21230___
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http://linked.open...avai/riv/aktivita
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http://linked.open...avai/riv/aktivity
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http://linked.open...vai/riv/dodaniDat
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http://linked.open...aciTvurceVysledku
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http://linked.open.../riv/druhVysledku
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http://linked.open...iv/duvernostUdaju
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http://linked.open...titaPredkladatele
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http://linked.open...dnocenehoVysledku
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http://linked.open...ai/riv/idVysledku
| - RIV/68407700:21230/12:00200377
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http://linked.open...riv/jazykVysledku
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http://linked.open.../riv/klicovaSlova
| - Motion; Tracking and Video Analysis; Classification and Clustering (en)
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http://linked.open.../riv/klicoveSlovo
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http://linked.open...ontrolniKodProRIV
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http://linked.open...v/mistoKonaniAkce
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http://linked.open...i/riv/mistoVydani
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http://linked.open...i/riv/nazevZdroje
| - ICPR 2012: Proceedings of 21st International Conference on Pattern Recognition
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http://linked.open...in/vavai/riv/obor
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http://linked.open...ichTvurcuVysledku
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http://linked.open...cetTvurcuVysledku
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http://linked.open...vavai/riv/projekt
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http://linked.open...UplatneniVysledku
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http://linked.open...iv/tvurceVysledku
| - Hlaváč, Václav
- Cerman, Lukáš
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http://linked.open...vavai/riv/typAkce
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http://linked.open.../riv/zahajeniAkce
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number of pages
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http://purl.org/ne...btex#hasPublisher
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https://schema.org/isbn
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http://localhost/t...ganizacniJednotka
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