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dc.date.accessioned 2020-02-17T15:30:27Z
dc.date.available 2020-02-17T15:30:27Z
dc.date.issued 2019
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/89144
dc.description.abstract 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. en
dc.format.extent 7-12 es
dc.language en es
dc.subject Action recognition es
dc.subject Convolutional neural network es
dc.subject Long short-term memory es
dc.subject UCF-11 es
dc.title CNN-LSTM Architecture for Action Recognition in Videos en
dc.type Objeto de conferencia es
sedici.identifier.issn 2683-8990 es
sedici.creator.person Orozco, Carlos Ismael es
sedici.creator.person Buemi, María E. es
sedici.creator.person Berlles, Julio Jacobo es
sedici.subject.materias Ciencias Informáticas es
sedici.description.fulltext true es
mods.originInfo.place Sociedad Argentina de Informática e Investigación Operativa es
sedici.subtype Objeto de conferencia es
sedici.rights.license Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)
sedici.rights.uri http://creativecommons.org/licenses/by-nc-sa/3.0/
sedici.date.exposure 2019-09
sedici.relation.event I Simposio Argentino de Imágenes y Visión (SAIV 2019) - JAIIO 48 (Salta) es
sedici.description.peerReview peer-review es


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Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0) Excepto donde se diga explícitamente, este item se publica bajo la siguiente licencia Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)