Pedestrian tracking using probability fields and a movement feature space1

Retrieving useful information from video sequences, such as the dynamics of pedestrians, and other moving objects on a video sequence, leads to further knowledge of what is happening on a scene. In this paper, a Target Framework associates each person with an autonomous entity, modeling its trajecto...

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Autores:
Negri, Pablo
Garayalde, Damian
Tipo de recurso:
Article of journal
Fecha de publicación:
2017
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/60446
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/60446
http://bdigital.unal.edu.co/58778/
Palabra clave:
62 Ingeniería y operaciones afines / Engineering
pedestrian tracking
movement feature space
target framework
seguimiento de peatones
espacio de descriptores dinámicos
target framework
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
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spelling Atribución-NoComercial 4.0 InternacionalDerechos reservados - Universidad Nacional de Colombiahttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Negri, Pabloe4ff107c-89d1-4684-95e9-48073d2c6560300Garayalde, Damianbbcea9aa-192d-4780-95f6-b29f9aec05293002019-07-02T18:20:12Z2019-07-02T18:20:12Z2017-01-01ISSN: 2346-2183https://repositorio.unal.edu.co/handle/unal/60446http://bdigital.unal.edu.co/58778/Retrieving useful information from video sequences, such as the dynamics of pedestrians, and other moving objects on a video sequence, leads to further knowledge of what is happening on a scene. In this paper, a Target Framework associates each person with an autonomous entity, modeling its trajectory and speed by using a state machine. The particularity of our methodology is the use of a Movement Feature Space (MFS) to generate descriptors for classifiers and trackers. This approach is applied to two public sequences (PETS2009 and TownCentre). The results of this tracking outperform other algorithms reported in the literature, which have, however, a higher computational complexity.Recuperar información de secuencias de video, como la dinámica de peatones u otros objetos en movimiento en la escena, representa una herramienta indispensable para interpretar que está ocurriendo en la escena. Este artículo propone el uso de una Arquitectura basada en Targets, que asocian a cada persona una entidad autónoma y modeliza su dinámica con una máquina de estados. Nuestra metodología utiliza una familia de descriptores calculados en el Movement Feature Space (MFS) para realizar la detección y seguimiento de las personas. Esta arquitectura fue evaluada usando dos bases de datos públicas (PETS2009 y TownCentre), y comparándola con algoritmos de la literatura, arrojó mejores resultados, aun cuando estos algoritmos poseen una mayor complejidad computacional.application/pdfspaUniversidad Nacional de Colombia (Sede Medellín). Facultad de Minas.https://revistas.unal.edu.co/index.php/dyna/article/view/57028Universidad Nacional de Colombia Revistas electrónicas UN DynaDynaNegri, Pablo and Garayalde, Damian (2017) Pedestrian tracking using probability fields and a movement feature space1. DYNA, 84 (200). pp. 217-227. ISSN 2346-218362 Ingeniería y operaciones afines / Engineeringpedestrian trackingmovement feature spacetarget frameworkseguimiento de peatonesespacio de descriptores dinámicostarget frameworkPedestrian tracking using probability fields and a movement feature space1Artículo de revistainfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Texthttp://purl.org/redcol/resource_type/ARTORIGINAL57028-321210-2-PB.pdfapplication/pdf1257125https://repositorio.unal.edu.co/bitstream/unal/60446/1/57028-321210-2-PB.pdf91e53ca3af424b47d9bdf2af9497d717MD51THUMBNAIL57028-321210-2-PB.pdf.jpg57028-321210-2-PB.pdf.jpgGenerated Thumbnailimage/jpeg9656https://repositorio.unal.edu.co/bitstream/unal/60446/2/57028-321210-2-PB.pdf.jpg7efdf2dce29deab52536e23101baea89MD52unal/60446oai:repositorio.unal.edu.co:unal/604462023-04-07 23:04:16.925Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.co
dc.title.spa.fl_str_mv Pedestrian tracking using probability fields and a movement feature space1
title Pedestrian tracking using probability fields and a movement feature space1
spellingShingle Pedestrian tracking using probability fields and a movement feature space1
62 Ingeniería y operaciones afines / Engineering
pedestrian tracking
movement feature space
target framework
seguimiento de peatones
espacio de descriptores dinámicos
target framework
title_short Pedestrian tracking using probability fields and a movement feature space1
title_full Pedestrian tracking using probability fields and a movement feature space1
title_fullStr Pedestrian tracking using probability fields and a movement feature space1
title_full_unstemmed Pedestrian tracking using probability fields and a movement feature space1
title_sort Pedestrian tracking using probability fields and a movement feature space1
dc.creator.fl_str_mv Negri, Pablo
Garayalde, Damian
dc.contributor.author.spa.fl_str_mv Negri, Pablo
Garayalde, Damian
dc.subject.ddc.spa.fl_str_mv 62 Ingeniería y operaciones afines / Engineering
topic 62 Ingeniería y operaciones afines / Engineering
pedestrian tracking
movement feature space
target framework
seguimiento de peatones
espacio de descriptores dinámicos
target framework
dc.subject.proposal.spa.fl_str_mv pedestrian tracking
movement feature space
target framework
seguimiento de peatones
espacio de descriptores dinámicos
target framework
description Retrieving useful information from video sequences, such as the dynamics of pedestrians, and other moving objects on a video sequence, leads to further knowledge of what is happening on a scene. In this paper, a Target Framework associates each person with an autonomous entity, modeling its trajectory and speed by using a state machine. The particularity of our methodology is the use of a Movement Feature Space (MFS) to generate descriptors for classifiers and trackers. This approach is applied to two public sequences (PETS2009 and TownCentre). The results of this tracking outperform other algorithms reported in the literature, which have, however, a higher computational complexity.
publishDate 2017
dc.date.issued.spa.fl_str_mv 2017-01-01
dc.date.accessioned.spa.fl_str_mv 2019-07-02T18:20:12Z
dc.date.available.spa.fl_str_mv 2019-07-02T18:20:12Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.issn.spa.fl_str_mv ISSN: 2346-2183
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identifier_str_mv ISSN: 2346-2183
url https://repositorio.unal.edu.co/handle/unal/60446
http://bdigital.unal.edu.co/58778/
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.spa.fl_str_mv https://revistas.unal.edu.co/index.php/dyna/article/view/57028
dc.relation.ispartof.spa.fl_str_mv Universidad Nacional de Colombia Revistas electrónicas UN Dyna
Dyna
dc.relation.references.spa.fl_str_mv Negri, Pablo and Garayalde, Damian (2017) Pedestrian tracking using probability fields and a movement feature space1. DYNA, 84 (200). pp. 217-227. ISSN 2346-2183
dc.rights.spa.fl_str_mv Derechos reservados - Universidad Nacional de Colombia
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv Atribución-NoComercial 4.0 Internacional
dc.rights.uri.spa.fl_str_mv http://creativecommons.org/licenses/by-nc/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución-NoComercial 4.0 Internacional
Derechos reservados - Universidad Nacional de Colombia
http://creativecommons.org/licenses/by-nc/4.0/
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Universidad Nacional de Colombia (Sede Medellín). Facultad de Minas.
institution Universidad Nacional de Colombia
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