DeepVI: A Novel Framework for Learning Deep View-Invariant Human Action Representations using a Single RGB Camera

Reference : DeepVI: A Novel Framework for Learning Deep View-Invariant Human Action Representatio...


	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/42471

Title :	DeepVI: A Novel Framework for Learning Deep View-Invariant Human Action Representations using a Single RGB Camera
Language :	English
Author, co-author :	Papadopoulos, Konstantinos [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Ghorbel, Enjie [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Oyedotun, Oyebade [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Aouada, Djamila [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Ottersten, Björn [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Publication date :	2020
Main document title :	IEEE International Conference on Automatic Face and Gesture Recognition, Buenos Aires 18-22 May 2020
Peer reviewed :	Yes
Event name :	IEEE International Conference on Automatic Face and Gesture Recognition
Event date :	from 18-05-2020 to 22-05-2020
Permalink :	http://hdl.handle.net/10993/42471
FnR project :	FnR ; FNR10415355 > Bjorn Ottersten > 3D-ACT > 3D Action Recognition Using Refinement and Invariance Strategies for Reliable Surveillance > 01/06/2016 > 31/05/2019 > 2015

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