Classifying Behaviours in Videos with Recurrent Neural Networks

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Title: Classifying Behaviours in Videos with Recurrent Neural Networks
Authors: Abellan-Abenza, Javier | Garcia-Garcia, Alberto | Oprea, Sergiu | Ivorra-Piqueres, David | Garcia-Rodriguez, Jose
Research Group/s: Informática Industrial y Redes de Computadores
Center, Department or Service: Universidad de Alicante. Departamento de Tecnología Informática y Computación
Keywords: Convolutional Neural Networks | Human Behaviour | Long-Short Term Memory | Recurrent Neural Networks | RGB-D Cameras
Knowledge Area: Arquitectura y Tecnología de Computadores
Issue Date: 2017
Publisher: IGI Global
Citation: International Journal of Computer Vision and Image Processing. 2017, 7(4): 1-14. doi:10.4018/IJCVIP.2017100101
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.
Sponsor: This work has been funded by the Spanish Government TIN2016-76515-R grant for the COMBAHO project, supported with Feder funds.
ISSN: 2155-6997 (Print) | 2155-6989 (Online)
DOI: 10.4018/IJCVIP.2017100101
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2017, IGI Global
Peer Review: si
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Appears in Collections:INV - I2RC - Artículos de Revistas

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