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dc.contributor.author
Arnaud, Elise
dc.contributor.author
Memin, Etienne
dc.contributor.author
Cernuschi Frias, Bruno
dc.date.available
2020-07-22T15:09:05Z
dc.date.issued
2005-01
dc.identifier.citation
Arnaud, Elise; Memin, Etienne; Cernuschi Frias, Bruno; Conditional filters for image sequence-based tracking - application to point tracking; Institute of Electrical and Electronics Engineers; Ieee Transactions on Image Processing; 14; 1; 1-2005; 63-79
dc.identifier.issn
1057-7149
dc.identifier.uri
http://hdl.handle.net/11336/109858
dc.description.abstract
A new conditional formulation of classical filtering methods is proposed. This formulation is dedicated to image sequence-based tracking. These conditional filters allow solving systems whose measurements and state equation are estimated from the image data. In particular, the model that is considered for point tracking combines a state equation relying on the optical flow constraint and measurements provided by a matching technique. Based on this, two point trackers are derived. The first one is a linear tracker well suited to image sequences exhibiting global-dominant motion. This filter is determined through the use of a new estimator, called the conditional linear minimum variance estimator. The second one is a nonlinear tracker, implemented from a conditional particle filter. It allows tracking of points whose motion may be only locally described. These conditional trackers significantly improve results in some general situations. In particular, they allow for dealing with noisy sequences, abrupt changes of trajectories, occlusions, and cluttered background.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Institute of Electrical and Electronics Engineers
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
POINT TRACKING
dc.subject
STOCHASTIC FILTERING
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MINIMUM VARIANCE ESTIMATOR
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PARTICLE FILTERING
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OPTIMAL IMPORTANCE FUNCTION
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ROBUST MOTION ESTIMATION
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CORRELATION MEASUREMENT
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GATING
dc.subject.classification
Ingeniería Eléctrica y Electrónica
dc.subject.classification
Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información
dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
dc.title
Conditional filters for image sequence-based tracking - application to point tracking
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2020-05-11T19:04:14Z
dc.journal.volume
14
dc.journal.number
1
dc.journal.pagination
63-79
dc.journal.pais
Estados Unidos
dc.description.fil
Fil: Arnaud, Elise. Universite de Rennes I; Francia
dc.description.fil
Fil: Memin, Etienne. Universite de Rennes I; Francia
dc.description.fil
Fil: Cernuschi Frias, Bruno. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderón; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina
dc.journal.title
Ieee Transactions on Image Processing
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://ieeexplore.ieee.org/document/1369330
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1109/TIP.2004.838707
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