Robust multiple-people tracking using color-based particle filters

Robust and accurate people tracking is a key task in many promising computer-vision applications. One must deal with non-rigid targets in open-world scenarios, whose shape and appearance evolve over time. Targets may interact, causing partial or complete occlusions. This paper improves tracking by m...

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Detalles Bibliográficos
Autores: Rowe, Daniel, Huerta Casado, Iván, Gonzàlez, Jordi, Villanueva, Juan J.
Tipo de recurso: capítulo de libro
Fecha de publicación:2007
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/2687
Acceso en línea:https://hdl.handle.net/2117/2687
Access Level:acceso abierto
Palabra clave:Computer vision
Visió per ordinador
Classificació INSPEC::Pattern recognition::Computer vision
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo
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repository_id_str
spelling Robust multiple-people tracking using color-based particle filtersRowe, DanielHuerta Casado, IvánGonzàlez, JordiVillanueva, Juan J.Computer visionVisió per ordinadorClassificació INSPEC::Pattern recognition::Computer visionÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeoRobust and accurate people tracking is a key task in many promising computer-vision applications. One must deal with non-rigid targets in open-world scenarios, whose shape and appearance evolve over time. Targets may interact, causing partial or complete occlusions. This paper improves tracking by means of particle filtering, where occlusions are handled considering the target's predicted trajectories. Model drift is tackled by careful updating, based on the history of likelihood measures. A colour-based likelihood, computed from histogram similarity, is used. Experiments are carried out using sequences from the CAVIAR database.Peer ReviewedSpringer Verlag20072007-01-0120092009-03-13book parthttp://purl.org/coar/resource_type/c_3248NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/bookPartapplication/pdfhttps://hdl.handle.net/2117/2687reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/26872026-05-27T15:37:01Z
dc.title.none.fl_str_mv Robust multiple-people tracking using color-based particle filters
title Robust multiple-people tracking using color-based particle filters
spellingShingle Robust multiple-people tracking using color-based particle filters
Rowe, Daniel
Computer vision
Visió per ordinador
Classificació INSPEC::Pattern recognition::Computer vision
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo
title_short Robust multiple-people tracking using color-based particle filters
title_full Robust multiple-people tracking using color-based particle filters
title_fullStr Robust multiple-people tracking using color-based particle filters
title_full_unstemmed Robust multiple-people tracking using color-based particle filters
title_sort Robust multiple-people tracking using color-based particle filters
dc.creator.none.fl_str_mv Rowe, Daniel
Huerta Casado, Iván
Gonzàlez, Jordi
Villanueva, Juan J.
author Rowe, Daniel
author_facet Rowe, Daniel
Huerta Casado, Iván
Gonzàlez, Jordi
Villanueva, Juan J.
author_role author
author2 Huerta Casado, Iván
Gonzàlez, Jordi
Villanueva, Juan J.
author2_role author
author
author
dc.subject.none.fl_str_mv Computer vision
Visió per ordinador
Classificació INSPEC::Pattern recognition::Computer vision
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo
topic Computer vision
Visió per ordinador
Classificació INSPEC::Pattern recognition::Computer vision
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo
description Robust and accurate people tracking is a key task in many promising computer-vision applications. One must deal with non-rigid targets in open-world scenarios, whose shape and appearance evolve over time. Targets may interact, causing partial or complete occlusions. This paper improves tracking by means of particle filtering, where occlusions are handled considering the target's predicted trajectories. Model drift is tackled by careful updating, based on the history of likelihood measures. A colour-based likelihood, computed from histogram similarity, is used. Experiments are carried out using sequences from the CAVIAR database.
publishDate 2007
dc.date.none.fl_str_mv 2007
2007-01-01
2009
2009-03-13
dc.type.none.fl_str_mv book part
http://purl.org/coar/resource_type/c_3248
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/bookPart
format bookPart
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/2687
url https://hdl.handle.net/2117/2687
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer Verlag
publisher.none.fl_str_mv Springer Verlag
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869403018496049152
score 15,301603

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