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Authors: Anderson Carlos Sousa e Santos 1 ; Helena de Almeida Maia 1 ; Marcos Roberto e Souza 1 ; Marcelo Bernardes Vieira 2 and Helio Pedrini 1

Affiliations: 1 Institute of Computing, University of Campinas, Campinas-SP, 13083-852, Brazil ; 2 Federal University of Juiz de Fora, Juiz de Fora-MG, 36036-900, Brazil

Keyword(s): Action Recognition, Multi-stream Neural Network, Video Representation, Fuzzy Fusion.

Abstract: There are several aspects that may help in the characterization of an action being performed in a video, such as scene appearance and estimated movement of the involved objects. Many works in the literature combine different aspects to recognize the actions, which has shown to be superior than individual results. Just as important as the definition of representative and complementary aspects is the choice of good combination methods that exploit the strengths of each aspect. In this work, we propose a novel fusion strategy based on two fuzzy integral methods. This strategy is capable of generalizing other common operators, besides it allows more combinations to be evaluated by having a distinct impact in sets linearly dependent. Our experiments show that the fuzzy fusion outperforms the most commonly-used weighted average on the challenging UCF101 and HMDB51 datasets.

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Paper citation in several formats:
Santos, A.; Maia, H.; Souza, M.; Vieira, M. and Pedrini, H. (2020). Fuzzy Fusion for Two-stream Action Recognition. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP; ISBN 978-989-758-402-2; ISSN 2184-4321, SciTePress, pages 117-123. DOI: 10.5220/0008973901170123

@conference{visapp20,
author={Anderson Carlos Sousa e Santos. and Helena de Almeida Maia. and Marcos Roberto e Souza. and Marcelo Bernardes Vieira. and Helio Pedrini.},
title={Fuzzy Fusion for Two-stream Action Recognition},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP},
year={2020},
pages={117-123},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008973901170123},
isbn={978-989-758-402-2},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP
TI - Fuzzy Fusion for Two-stream Action Recognition
SN - 978-989-758-402-2
IS - 2184-4321
AU - Santos, A.
AU - Maia, H.
AU - Souza, M.
AU - Vieira, M.
AU - Pedrini, H.
PY - 2020
SP - 117
EP - 123
DO - 10.5220/0008973901170123
PB - SciTePress

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