CNN-LSTM Architecture for Action Recognition in Videos

Autores
Orozco, Carlos Ismael; Buemi, María E.; Berlles, Julio Jacobo
Año de publicación
2019
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Action recognition in videos is currently a topic of interest in the area of computer vision, due to potential applications such as: multimedia indexing, surveillance in public spaces, among others. In this paper we propose a CNN{LSTM architecture. First, a pre-trained VGG16 convolutional neuronal networks extracts the features of the input video. Then, a LSTM classi es the video in a particular class. To carry out the training and the test, we used the UCF-11 dataset. Evaluate the performance of our system using the evaluation metric in accuracy. We apply LOOCV with k = 25, we obtain ~ 98% and ~ 91% for training and test respectively.
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
Action recognition
Convolutional neural network
Long short-term memory
UCF-11
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/89144
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/89144
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling CNN-LSTM Architecture for Action Recognition in VideosOrozco, Carlos IsmaelBuemi, María E.Berlles, Julio JacoboCiencias InformáticasAction recognitionConvolutional neural networkLong short-term memoryUCF-11Action recognition in videos is currently a topic of interest in the area of computer vision, due to potential applications such as: multimedia indexing, surveillance in public spaces, among others. In this paper we propose a CNN{LSTM architecture. First, a pre-trained VGG16 convolutional neuronal networks extracts the features of the input video. Then, a LSTM classi es the video in a particular class. To carry out the training and the test, we used the UCF-11 dataset. Evaluate the performance of our system using the evaluation metric in accuracy. We apply LOOCV with k = 25, we obtain ~ 98% and ~ 91% for training and test respectively.Sociedad Argentina de Informática e Investigación Operativa2019-09info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf7-12http://sedici.unlp.edu.ar/handle/10915/89144enginfo:eu-repo/semantics/altIdentifier/issn/2683-8990info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/3.0/Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:18:18Zoai:sedici.unlp.edu.ar:10915/89144Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:18:19.025SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv CNN-LSTM Architecture for Action Recognition in Videos
title CNN-LSTM Architecture for Action Recognition in Videos
spellingShingle CNN-LSTM Architecture for Action Recognition in Videos
Orozco, Carlos Ismael
Ciencias Informáticas
Action recognition
Convolutional neural network
Long short-term memory
UCF-11
title_short CNN-LSTM Architecture for Action Recognition in Videos
title_full CNN-LSTM Architecture for Action Recognition in Videos
title_fullStr CNN-LSTM Architecture for Action Recognition in Videos
title_full_unstemmed CNN-LSTM Architecture for Action Recognition in Videos
title_sort CNN-LSTM Architecture for Action Recognition in Videos
dc.creator.none.fl_str_mv Orozco, Carlos Ismael
Buemi, María E.
Berlles, Julio Jacobo
author Orozco, Carlos Ismael
author_facet Orozco, Carlos Ismael
Buemi, María E.
Berlles, Julio Jacobo
author_role author
author2 Buemi, María E.
Berlles, Julio Jacobo
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Action recognition
Convolutional neural network
Long short-term memory
UCF-11
topic Ciencias Informáticas
Action recognition
Convolutional neural network
Long short-term memory
UCF-11
dc.description.none.fl_txt_mv Action recognition in videos is currently a topic of interest in the area of computer vision, due to potential applications such as: multimedia indexing, surveillance in public spaces, among others. In this paper we propose a CNN{LSTM architecture. First, a pre-trained VGG16 convolutional neuronal networks extracts the features of the input video. Then, a LSTM classi es the video in a particular class. To carry out the training and the test, we used the UCF-11 dataset. Evaluate the performance of our system using the evaluation metric in accuracy. We apply LOOCV with k = 25, we obtain ~ 98% and ~ 91% for training and test respectively.
Sociedad Argentina de Informática e Investigación Operativa
description Action recognition in videos is currently a topic of interest in the area of computer vision, due to potential applications such as: multimedia indexing, surveillance in public spaces, among others. In this paper we propose a CNN{LSTM architecture. First, a pre-trained VGG16 convolutional neuronal networks extracts the features of the input video. Then, a LSTM classi es the video in a particular class. To carry out the training and the test, we used the UCF-11 dataset. Evaluate the performance of our system using the evaluation metric in accuracy. We apply LOOCV with k = 25, we obtain ~ 98% and ~ 91% for training and test respectively.
publishDate 2019
dc.date.none.fl_str_mv 2019-09
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/10915/89144
url http://sedici.unlp.edu.ar/handle/10915/89144
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/2683-8990
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/3.0/
Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/3.0/
Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)
dc.format.none.fl_str_mv application/pdf
7-12
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 13.069144

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