Mixed-state causal modeling for statistical KL-based motion texture tracking

Autores
Crivelli, Tomás; Cernuschi Frias, Bruno; Bouthemy, Patrick; Yao, Jian-Feng
Año de publicación
2010
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We are interested in the modeling and tracking of dynamic or motion textures, which refer to dynamic contents that can be classified as a texture with motion (fire, smoke, crowd of people). Experimentally we observe that they depict motion maps with values of a mixed type: a discrete value at zero (absence of motion) and continuous non-null motion values. We thus introduce a temporal mixed-state Markov model for the characterization of motion textures from which a set of 13 parameters is extracted as the descriptive feature of the dynamic content. Then, a motion texture tracking strategy is proposed using the conditional Kullback?Leibler (KL) divergence between mixed-state probability densities, which allows us to estimate the position using a statistical matching approach.
Fil: Crivelli, Tomás. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Electronica; Argentina
Fil: Cernuschi Frias, Bruno. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderon; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Electronica; Argentina
Fil: Bouthemy, Patrick. Irisa, Inria, Rennes, Francia;
Fil: Yao, Jian-Feng. Institut National de Recherche en Informatique et en Automatique; Francia
Materia
Mixed-State Markov Models
Motion Textures
Visual Tracking
Kullback-Leibler Divergence
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/19432
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Mixed-state causal modeling for statistical KL-based motion texture trackingCrivelli, TomásCernuschi Frias, BrunoBouthemy, PatrickYao, Jian-FengMixed-State Markov ModelsMotion TexturesVisual TrackingKullback-Leibler Divergencehttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2We are interested in the modeling and tracking of dynamic or motion textures, which refer to dynamic contents that can be classified as a texture with motion (fire, smoke, crowd of people). Experimentally we observe that they depict motion maps with values of a mixed type: a discrete value at zero (absence of motion) and continuous non-null motion values. We thus introduce a temporal mixed-state Markov model for the characterization of motion textures from which a set of 13 parameters is extracted as the descriptive feature of the dynamic content. Then, a motion texture tracking strategy is proposed using the conditional Kullback?Leibler (KL) divergence between mixed-state probability densities, which allows us to estimate the position using a statistical matching approach.Fil: Crivelli, Tomás. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Electronica; ArgentinaFil: Cernuschi Frias, Bruno. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderon; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Electronica; ArgentinaFil: Bouthemy, Patrick. Irisa, Inria, Rennes, Francia;Fil: Yao, Jian-Feng. Institut National de Recherche en Informatique et en Automatique; FranciaElsevier Science2010-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/19432Crivelli, Tomás; Cernuschi Frias, Bruno; Bouthemy, Patrick; Yao, Jian-Feng; Mixed-state causal modeling for statistical KL-based motion texture tracking; Elsevier Science; Pattern Recognition Letters; 31; 14; 11-2010; 2286-22940167-8655CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0167865510002035info:eu-repo/semantics/altIdentifier/doi/10.1016/j.patrec.2010.06.016info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-29T11:14:31Zoai:ri.conicet.gov.ar:11336/19432instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-29 11:14:31.367CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Mixed-state causal modeling for statistical KL-based motion texture tracking
title Mixed-state causal modeling for statistical KL-based motion texture tracking
spellingShingle Mixed-state causal modeling for statistical KL-based motion texture tracking
Crivelli, Tomás
Mixed-State Markov Models
Motion Textures
Visual Tracking
Kullback-Leibler Divergence
title_short Mixed-state causal modeling for statistical KL-based motion texture tracking
title_full Mixed-state causal modeling for statistical KL-based motion texture tracking
title_fullStr Mixed-state causal modeling for statistical KL-based motion texture tracking
title_full_unstemmed Mixed-state causal modeling for statistical KL-based motion texture tracking
title_sort Mixed-state causal modeling for statistical KL-based motion texture tracking
dc.creator.none.fl_str_mv Crivelli, Tomás
Cernuschi Frias, Bruno
Bouthemy, Patrick
Yao, Jian-Feng
author Crivelli, Tomás
author_facet Crivelli, Tomás
Cernuschi Frias, Bruno
Bouthemy, Patrick
Yao, Jian-Feng
author_role author
author2 Cernuschi Frias, Bruno
Bouthemy, Patrick
Yao, Jian-Feng
author2_role author
author
author
dc.subject.none.fl_str_mv Mixed-State Markov Models
Motion Textures
Visual Tracking
Kullback-Leibler Divergence
topic Mixed-State Markov Models
Motion Textures
Visual Tracking
Kullback-Leibler Divergence
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv We are interested in the modeling and tracking of dynamic or motion textures, which refer to dynamic contents that can be classified as a texture with motion (fire, smoke, crowd of people). Experimentally we observe that they depict motion maps with values of a mixed type: a discrete value at zero (absence of motion) and continuous non-null motion values. We thus introduce a temporal mixed-state Markov model for the characterization of motion textures from which a set of 13 parameters is extracted as the descriptive feature of the dynamic content. Then, a motion texture tracking strategy is proposed using the conditional Kullback?Leibler (KL) divergence between mixed-state probability densities, which allows us to estimate the position using a statistical matching approach.
Fil: Crivelli, Tomás. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Electronica; Argentina
Fil: Cernuschi Frias, Bruno. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderon; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Electronica; Argentina
Fil: Bouthemy, Patrick. Irisa, Inria, Rennes, Francia;
Fil: Yao, Jian-Feng. Institut National de Recherche en Informatique et en Automatique; Francia
description We are interested in the modeling and tracking of dynamic or motion textures, which refer to dynamic contents that can be classified as a texture with motion (fire, smoke, crowd of people). Experimentally we observe that they depict motion maps with values of a mixed type: a discrete value at zero (absence of motion) and continuous non-null motion values. We thus introduce a temporal mixed-state Markov model for the characterization of motion textures from which a set of 13 parameters is extracted as the descriptive feature of the dynamic content. Then, a motion texture tracking strategy is proposed using the conditional Kullback?Leibler (KL) divergence between mixed-state probability densities, which allows us to estimate the position using a statistical matching approach.
publishDate 2010
dc.date.none.fl_str_mv 2010-11
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/19432
Crivelli, Tomás; Cernuschi Frias, Bruno; Bouthemy, Patrick; Yao, Jian-Feng; Mixed-state causal modeling for statistical KL-based motion texture tracking; Elsevier Science; Pattern Recognition Letters; 31; 14; 11-2010; 2286-2294
0167-8655
CONICET Digital
CONICET
url http://hdl.handle.net/11336/19432
identifier_str_mv Crivelli, Tomás; Cernuschi Frias, Bruno; Bouthemy, Patrick; Yao, Jian-Feng; Mixed-state causal modeling for statistical KL-based motion texture tracking; Elsevier Science; Pattern Recognition Letters; 31; 14; 11-2010; 2286-2294
0167-8655
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0167865510002035
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.patrec.2010.06.016
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1847426009501007872
score 13.10058

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