Video-based human action recognition using deep learning: a review

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dc.contributor.author Pham, Huy-Hieu
dc.contributor.author Khoudour, Louahdi
dc.contributor.author Crouzil, Alain
dc.contributor.author Zegers, Pablo
dc.contributor.author Velastin Carroza, Sergio Alejandro
dc.date.accessioned 2018-05-07T11:02:22Z
dc.date.available 2018-05-07T11:02:22Z
dc.date.issued 2015
dc.identifier.bibliographicCitation Pham, H.H., Khoudour, L., Crouzil, A., Zegers, P., Velastin, S.A. (2015). Video-based human action recognition using deep learning: a review, pp. 1-34.
dc.identifier.uri http://hdl.handle.net/10016/26542
dc.description.abstract Human action recognition is an important application domain in computer vision. Its primary aim is to accurately describe human actions and their interactions from a previously unseen data sequence acquired by sensors. The ability to recognize, understand and predict complex human actions enables the construction of many important applications such as intelligent surveillance systems, human-computer interfaces, health care, security and military applications. In recent years, deep learning has been given particular attention by the computer vision community. This paper presents an overview of the current state-of-the-art in action recognition using video analysis with deep learning techniques. We present the most important deep learning models for recognizing human actions, analyze them to provide the current progress of deep learning algorithms applied to solve human action recognition problems in realistic videos highlighting their advantages and disadvantages. Based on the quantitative analysis using recognition accuracies reported in the literature, our study identies state-of-the-art deep architectures in action recognition and then provides current trends and open problems for future works in this led.
dc.description.sponsorship This work was supported by the Cen-tre d'Etudes et d'Expertise sur les Risques, l'environnement la mobilité et l'aménagement (CEREMA) and the UC3M Conex-Marie Curie Program.
dc.format.extent 35
dc.format.mimetype application/pdf
dc.language.iso eng
dc.rights © 2015 Autores
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Human action recognition
dc.subject.other Deep learning
dc.subject.other CNNs
dc.subject.other RNN-LSTMs
dc.subject.other DBNs
dc.subject.other SDAs
dc.title Video-based human action recognition using deep learning: a review
dc.type preprint
dc.description.status No publicado
dc.subject.eciencia Informática
dc.rights.accessRights openAccess
dc.type.version draft
dc.identifier.uxxi DT/0000001607
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