Classifying Behaviours in Videos with Recurrent Neural Networks
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Campo DC | Valor | Idioma |
---|---|---|
dc.contributor | Informática Industrial y Redes de Computadores | es_ES |
dc.contributor.author | Abellan-Abenza, Javier | - |
dc.contributor.author | Garcia-Garcia, Alberto | - |
dc.contributor.author | Oprea, Sergiu | - |
dc.contributor.author | Ivorra-Piqueres, David | - |
dc.contributor.author | Garcia-Rodriguez, Jose | - |
dc.contributor.other | Universidad de Alicante. Departamento de Tecnología Informática y Computación | es_ES |
dc.date.accessioned | 2018-05-17T11:27:09Z | - |
dc.date.available | 2018-05-17T11:27:09Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | International Journal of Computer Vision and Image Processing. 2017, 7(4): 1-14. doi:10.4018/IJCVIP.2017100101 | es_ES |
dc.identifier.issn | 2155-6997 (Print) | - |
dc.identifier.issn | 2155-6989 (Online) | - |
dc.identifier.uri | http://hdl.handle.net/10045/75589 | - |
dc.description.abstract | This article describes how the human activity recognition in videos is a very attractive topic among researchers due to vast possible applications. This article considers the analysis of behaviors and activities in videos obtained with low-cost RGB cameras. To do this, a system is developed where a video is input, and produces as output the possible activities happening in the video. This information could be used in many applications such as video surveillance, disabled person assistance, as a home assistant, employee monitoring, etc. The developed system makes use of the successful techniques of Deep Learning. In particular, convolutional neural networks are used to detect features in the video images, meanwhile Recurrent Neural Networks are used to analyze these features and predict the possible activity in the video. | es_ES |
dc.description.sponsorship | This work has been funded by the Spanish Government TIN2016-76515-R grant for the COMBAHO project, supported with Feder funds. | es_ES |
dc.language | eng | es_ES |
dc.publisher | IGI Global | es_ES |
dc.rights | © 2017, IGI Global | es_ES |
dc.subject | Convolutional Neural Networks | es_ES |
dc.subject | Human Behaviour | es_ES |
dc.subject | Long-Short Term Memory | es_ES |
dc.subject | Recurrent Neural Networks | es_ES |
dc.subject | RGB-D Cameras | es_ES |
dc.subject.other | Arquitectura y Tecnología de Computadores | es_ES |
dc.title | Classifying Behaviours in Videos with Recurrent Neural Networks | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.peerreviewed | si | es_ES |
dc.identifier.doi | 10.4018/IJCVIP.2017100101 | - |
dc.relation.publisherversion | https://doi.org/10.4018/IJCVIP.2017100101 | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2016-76515-R | - |
Aparece en las colecciones: | INV - I2RC - Artículos de Revistas INV - AIA - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
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2017_Abellan-Abenza_etal_IJCVIP.pdf | 3,27 MB | Adobe PDF | Abrir Vista previa | |
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