Semi-automatic object tracking in video sequences

Autores
Lecumberry, Federico; Pardo, Álvaro
Año de publicación
2005
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
In this paper we present a method for semi-automatic object tracking in video sequences using multiple features and a method for probabilistic relaxation to improve the tracking results producing smooth and accurate tracked borders. Starting from a given initial position of the object in the first frame the proposed method automatically tracks the object in the sequence modeling the a posteriori probabilities of a set of features such as: color, position and motion, depth, etc
III Workshop de Computación Gráfica, Imágenes y Visualización (WCGIV)
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Segmentation
Signal processing
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/23011
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/23011
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repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Semi-automatic object tracking in video sequencesLecumberry, FedericoPardo, ÁlvaroCiencias InformáticasSegmentationSignal processingIn this paper we present a method for semi-automatic object tracking in video sequences using multiple features and a method for probabilistic relaxation to improve the tracking results producing smooth and accurate tracked borders. Starting from a given initial position of the object in the first frame the proposed method automatically tracks the object in the sequence modeling the a posteriori probabilities of a set of features such as: color, position and motion, depth, etcIII Workshop de Computación Gráfica, Imágenes y Visualización (WCGIV)Red de Universidades con Carreras en Informática (RedUNCI)2005-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/23011enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-11-05T12:35:13Zoai:sedici.unlp.edu.ar:10915/23011Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-11-05 12:35:13.851SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Semi-automatic object tracking in video sequences
title Semi-automatic object tracking in video sequences
spellingShingle Semi-automatic object tracking in video sequences
Lecumberry, Federico
Ciencias Informáticas
Segmentation
Signal processing
title_short Semi-automatic object tracking in video sequences
title_full Semi-automatic object tracking in video sequences
title_fullStr Semi-automatic object tracking in video sequences
title_full_unstemmed Semi-automatic object tracking in video sequences
title_sort Semi-automatic object tracking in video sequences
dc.creator.none.fl_str_mv Lecumberry, Federico
Pardo, Álvaro
author Lecumberry, Federico
author_facet Lecumberry, Federico
Pardo, Álvaro
author_role author
author2 Pardo, Álvaro
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Segmentation
Signal processing
topic Ciencias Informáticas
Segmentation
Signal processing
dc.description.none.fl_txt_mv In this paper we present a method for semi-automatic object tracking in video sequences using multiple features and a method for probabilistic relaxation to improve the tracking results producing smooth and accurate tracked borders. Starting from a given initial position of the object in the first frame the proposed method automatically tracks the object in the sequence modeling the a posteriori probabilities of a set of features such as: color, position and motion, depth, etc
III Workshop de Computación Gráfica, Imágenes y Visualización (WCGIV)
Red de Universidades con Carreras en Informática (RedUNCI)
description In this paper we present a method for semi-automatic object tracking in video sequences using multiple features and a method for probabilistic relaxation to improve the tracking results producing smooth and accurate tracked borders. Starting from a given initial position of the object in the first frame the proposed method automatically tracks the object in the sequence modeling the a posteriori probabilities of a set of features such as: color, position and motion, depth, etc
publishDate 2005
dc.date.none.fl_str_mv 2005-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/23011
url http://sedici.unlp.edu.ar/handle/10915/23011
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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score 13.087074

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