CNN-LSTM Architecture for Action Recognition in Videos

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 n...

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Detalles Bibliográficos
Autores principales: Orozco, Carlos Ismael, Buemi, María E., Berlles, Julio Jacobo
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2019
Materias:
Ciencias Informáticas
Action recognition
Convolutional neural network
Long short-term memory
UCF-11
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/89144
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-89144
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Action recognition
Convolutional neural network
Long short-term memory
UCF-11
spellingShingle Ciencias Informáticas
Action recognition
Convolutional neural network
Long short-term memory
UCF-11
Orozco, Carlos Ismael
Buemi, María E.
Berlles, Julio Jacobo
CNN-LSTM Architecture for Action Recognition in Videos
topic_facet Ciencias Informáticas
Action recognition
Convolutional neural network
Long short-term memory
UCF-11
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.
format Objeto de conferencia
Objeto de conferencia
author Orozco, Carlos Ismael
Buemi, María E.
Berlles, Julio Jacobo
author_facet Orozco, Carlos Ismael
Buemi, María E.
Berlles, Julio Jacobo
author_sort Orozco, Carlos Ismael
title CNN-LSTM Architecture for Action Recognition in Videos
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
publishDate 2019
url http://sedici.unlp.edu.ar/handle/10915/89144
work_keys_str_mv AT orozcocarlosismael cnnlstmarchitectureforactionrecognitioninvideos
AT buemimariae cnnlstmarchitectureforactionrecognitioninvideos
AT berllesjuliojacobo cnnlstmarchitectureforactionrecognitioninvideos
bdutipo_str Repositorios
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