Trajectory Extraction and Deep Features for Classification of Liquid-gas Flow under the Context of Forced Oscillation

Luong Phat Nguyen; Julien Mille; Julien Mille; Dominique Li; Donatello Conte; Nicolas Ragot

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Paper

Trajectory Extraction and Deep Features for Classification of Liquid-gas Flow under the Context of Forced Oscillation

Topics: Deep Learning for Visual Understanding ; Features Extraction; Machine Learning Technologies for Vision

In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, 17-26, 2020 , Valletta, Malta

Authors: Luong Phat Nguyen ¹ ; Julien Mille ^{2

;

1} ; Dominique Li ¹ ; Donatello Conte ¹ and Nicolas Ragot ¹

Affiliations: ¹ Université de Tours, Tours, France ; ² INSA Centre Val de Loire, Blois, France

Keyword(s): Video Classification, Dynamic Texture, Deep Learning, Liquid-gas Flow.

Abstract: Computer vision and deep learning techniques are increasingly applied to analyze experimental processes in engineering domains. In this paper, we propose a new dataset of liquid-gas flow videos captured from a mechanical model simulating a cooling gallery of an automobile engine, through forced oscillations. The analysis of this dataset is of interest for fluid-mechanic field to validate the simulation environment. From computer vision point of view, it provides a new dynamic texture dataset with challenging tasks since liquid and gas keep changing constantly and the form of liquid-gas flow is closely related to the external environment. In particular predicting the rotation velocity of the engine corresponding to liquid-gas movements is a first step before precise analysis of flow patterns and of their trajectories. The paper also provides an experimental analysis showing that such rotation velocity can be hard to predict accurately. It could be achieved using deep learning approach es but not with state-of-the-art method dedicated to trajectory analysis. We show also that a preprocessing step with difference of Gaussian (DoG) over multiple scales as input of deep neural networks is mandatory to obtain satisfying results, up to 81.39% on the test set. This study opens an exploratory field for complex tasks on dynamic texture analysis such as trajectory analysis of heterogeneous masses. (More)

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Paper citation in several formats:

Nguyen, L.; Mille, J.; Li, D.; Conte, D. and Ragot, N. (2020). Trajectory Extraction and Deep Features for Classification of Liquid-gas Flow under the Context of Forced Oscillation. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP; ISBN 978-989-758-402-2; ISSN 2184-4321, SciTePress, pages 17-26. DOI: 10.5220/0008870700170026

@conference{visapp20,
author={Luong Phat Nguyen. and Julien Mille. and Dominique Li. and Donatello Conte. and Nicolas Ragot.},
title={Trajectory Extraction and Deep Features for Classification of Liquid-gas Flow under the Context of Forced Oscillation},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP},
year={2020},
pages={17-26},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008870700170026},
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 4: VISAPP
TI - Trajectory Extraction and Deep Features for Classification of Liquid-gas Flow under the Context of Forced Oscillation
SN - 978-989-758-402-2
IS - 2184-4321
AU - Nguyen, L.
AU - Mille, J.
AU - Li, D.
AU - Conte, D.
AU - Ragot, N.
PY - 2020
SP - 17
EP - 26
DO - 10.5220/0008870700170026
PB - SciTePress