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  4. Time-unfolding Object Existence Detection in Low-quality Underwater Videos using Convolutional Neural Networks
 
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2020
Conference Paper
Title

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

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 values of up to 97.7 %.
Author(s)
Tödtmann, Helmut  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Vahl, Matthias  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Lukas, Uwe von
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Ullrich, Torsten
Fraunhofer Austria / TU Graz CGV
Mainwork
15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. Proceedings. Vol.5: VISAPP  
Conference
International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) 2020  
International Conference on Computer Vision Theory and Applications (VISAPP) 2020  
Open Access
DOI
10.24406/publica-r-407929
10.5220/0008962803700377
File(s)
Download (975.01 KB)
Rights
CC BY-NC-ND 4.0: Creative Commons Attribution-NonCommercial-NoDerivatives
Language
English
Fraunhofer AUSTRIA  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Keyword(s)
  • Convolutional Neural Networks (CNN)

  • deep learning

  • environmental monitoring

  • detection

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