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dc.contributor.author
D'amato, Juan Pablo
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Dominguez, Leonardo Daniel
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Stramana, Franco Andrés
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Rubiales, Aldo Jose
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Pérez, Alejandro
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Figueroa García, Juan Carlos
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Díaz Gutiérrez, Yesid
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Gaona García, Elvis Eduardo
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Orjuela Cañón, Álvaro David
dc.date.available
2024-05-24T19:54:45Z
dc.date.issued
2021
dc.identifier.citation
An Hybrid CPU-GPU Parallel Multi-tracking Framework for Long-Term Video Sequences; 8th Workshop on Engineering Applications; Medellín; Colombia; 2021; 263-274
dc.identifier.isbn
978-3-030-86701-0
dc.identifier.issn
1865-0929
dc.identifier.uri
http://hdl.handle.net/11336/235941
dc.description.abstract
The automatic evaluation of video content is today one of the biggest challenges in computer Vision. When the purpose is to work with static surveillance cameras, where most of the time the scenes do not change ,a full Convolutional Network (CNN) approach seems to require too much CPU effort, specially when the objects are slightly moving between different frames. On the other side, visual tracking has seen great recent advances in either speed or accuracy but still remain scarce when have to deal with long videos where objects constantly new ones come into the scene and others disappear. In this paper, we present a parallelization scheme to handle multiple instances of object tracking. The main purpose is reduce overall processing time . The idea is to use already pre-trained CNNs for discovering objects and a parallel multi-tracker for following them, using both CPU and GPU devices. Our multi-tracker framework consists of three main components, a movement detector, an object classification and a tracker. We use the object detector as an initialization for trackers. When there are plenty of objects in the scene, the other two components are incorporated for reducing CPU effort. The first one is a scheduler than prioritizes tracking those objects that seems more relevant than the others. This scheduler use a criteria that balances the multi-tasking trying to reach the greatest speed-up with minimal detections lost. The second one, is a GPU memory handler, that lets adapt the framework to different hardware configuration specially when the CNNs could not be completely allocated into the device. As a general framework, it is very flexible and it could be customized with different trackers and CNN, adapting to different situations and platforms. We evaluate this framework in different cases and cameras configurations, reaching reasonable speed-up and confidence.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Springer
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
VIDEO PROCESSING
dc.subject
GPGPU
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OBJECT TRACKING
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CNN
dc.subject.classification
Ciencias de la Computación
dc.subject.classification
Ciencias de la Computación e Información
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CIENCIAS NATURALES Y EXACTAS
dc.title
An Hybrid CPU-GPU Parallel Multi-tracking Framework for Long-Term Video Sequences
dc.type
info:eu-repo/semantics/publishedVersion
dc.type
info:eu-repo/semantics/conferenceObject
dc.type
info:ar-repo/semantics/documento de conferencia
dc.date.updated
2022-11-04T15:30:32Z
dc.identifier.eissn
1865-0937
dc.journal.volume
1431
dc.journal.pagination
263-274
dc.journal.pais
Suiza
dc.journal.ciudad
Cham
dc.description.fil
Fil: D'amato, Juan Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina
dc.description.fil
Fil: Dominguez, Leonardo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina
dc.description.fil
Fil: Stramana, Franco Andrés. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina
dc.description.fil
Fil: Rubiales, Aldo Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina
dc.description.fil
Fil: Pérez, Alejandro. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/chapter/10.1007/978-3-030-86702-7_23
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1007/978-3-030-86702-7_23
dc.conicet.rol
Autor
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Autor
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Autor
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Autor
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Autor
dc.coverage
Internacional
dc.type.subtype
Workshop
dc.description.nombreEvento
8th Workshop on Engineering Applications
dc.date.evento
2021-10-06
dc.description.ciudadEvento
Medellín
dc.description.paisEvento
Colombia
dc.type.publicacion
Book
dc.description.institucionOrganizadora
Universidad Distrital Francisco José de Caldas
dc.description.institucionOrganizadora
Universidad Santo Tomás
dc.source.libro
Applied Computer Sciences in Engineering
dc.date.eventoHasta
2021-10-08
dc.type
Workshop
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