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Authors: Isaac Sanou ; Donatello Conte and Hubert Cardot

Affiliation: LiFAT, EA 6300, Université de Tours, 64 Avenue Jean Portalis, 37200, Tours and France

ISBN: 978-989-758-354-4

Keyword(s): Human Action Recognition, Deep Learning, 3D Convolution, Model Extensible.

Abstract: Human action Recognition has been extensively addressed by deep learning. However, the problem is still open and many deep learning architectures show some limits, such as extracting redundant spatio-temporal informations, using hand-crafted features, and instability of proposed networks on different datasets. In this paper, we present a general method of deep learning for the human action recognition. This model fits on any type of database and we apply it on CAD-120 which is a complex dataset. Our model thus clearly improves in two aspects. The first aspect is on the redundant informations and the second one is the generality and the multi-functionality application of our deep architecture. Our model uses only raw data for human action recognition and the approach achieves state-of-the-art action classification performance.

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Paper citation in several formats:
Sanou, I.; Conte, D. and Cardot, H. (2019). An Extensible Deep Architecture for Action Recognition Problem.In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5 VISAPP: VISAPP, ISBN 978-989-758-354-4, pages 191-199. DOI: 10.5220/0007253301910199

@conference{visapp19,
author={Isaac Sanou. and Conte, D. and Hubert Cardot.},
title={An Extensible Deep Architecture for Action Recognition Problem},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5 VISAPP: VISAPP,},
year={2019},
pages={191-199},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007253301910199},
isbn={978-989-758-354-4},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5 VISAPP: VISAPP,
TI - An Extensible Deep Architecture for Action Recognition Problem
SN - 978-989-758-354-4
AU - Sanou, I.
AU - Conte, D.
AU - Cardot, H.
PY - 2019
SP - 191
EP - 199
DO - 10.5220/0007253301910199

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