Time-unfolding Object Existence Detection in Low-quality Underwater Videos using Convolutional Neural Networks

Helmut Tödtmann; Helmut Tödtmann; Matthias Vahl; Uwe Freiherr von Lukas; Uwe Freiherr von Lukas; Torsten Ullrich; Torsten Ullrich

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Paper

Time-unfolding Object Existence Detection in Low-quality Underwater Videos using Convolutional Neural Networks

Topics: Deep Learning for Visual Understanding ; Machine Learning Technologies for Vision; Object Detection and Localization; Video Surveillance and Event Detection

In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, 370-377, 2020 , Valletta, Malta

Authors: Helmut Tödtmann ^{1

;

2} ; Matthias Vahl ¹ ; Uwe Freiherr von Lukas ^{1

;

3} and Torsten Ullrich ^{4

;

2}

Affiliations: ¹ Fraunhofer Institute for Computer Graphics Research IGD, Rostock, Germany ; ² Institute of Computer Graphics and Knowledge Visualization, Graz University of Technology, Austria ; ³ University of Rostock, Institute for Computer Science, Rostock, Germany ; ⁴ Fraunhofer Austria Research GmbH, Visual Computing Graz, Austria

Keyword(s): Convolutional Neural Network, Deep Learning, Environmental Monitoring, Implicit Segmentation, Detection.

Abstract: Monitoring the environment for early recognition of changes is necessary for assessing the success of renaturation measures on a facts basis. It is also used in fisheries and livestock production for monitoring and for quality assurance. The goal of the presented system is to count sea trouts annually over the course of several months. Sea trouts are detected with underwater camera systems triggered by motion sensors. Such a scenario generates many videos that have to be evaluated manually. This article describes the techniques used to automate the image evaluation process. An effective method has been developed to classify videos and determine the times of occurrence of sea trouts, while significantly reducing the annotation effort. A convolutional neural network has been trained via supervised learning. The underlying images are frame compositions automatically extracted from videos on which sea trouts are to be detected. The accuracy of the resulting detection system reaches value s of up to 97.7 %. (More)

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Paper citation in several formats:

Tödtmann, H.; Vahl, M.; von Lukas, U. and Ullrich, T. (2020). Time-unfolding Object Existence Detection in Low-quality Underwater Videos using Convolutional Neural Networks. 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 370-377. DOI: 10.5220/0008962803700377

@conference{visapp20,
author={Helmut Tödtmann. and Matthias Vahl. and Uwe Freiherr {von Lukas}. and Torsten Ullrich.},
title={Time-unfolding Object Existence Detection in Low-quality Underwater Videos using Convolutional Neural Networks},
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={370-377},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008962803700377},
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 - Time-unfolding Object Existence Detection in Low-quality Underwater Videos using Convolutional Neural Networks
SN - 978-989-758-402-2
IS - 2184-4321
AU - Tödtmann, H.
AU - Vahl, M.
AU - von Lukas, U.
AU - Ullrich, T.
PY - 2020
SP - 370
EP - 377
DO - 10.5220/0008962803700377
PB - SciTePress