Time-Unfolding Object Existence Detection in Low-Quality Underwater Videos using Convolutional Neural Networks

Helmut Tödtmann, Matthias Vahl, Uwe von Lukas, Torsten Ullrich

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandBegutachtung

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 %.
Originalspracheenglisch
Titel Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Redakteure/-innenGiovanni Maria Farinella, Petia Radeva, Jose Braz
Herausgeber (Verlag)SciTePress
Seiten370-377
Seitenumfang8
Band5, VISAPP
ISBN (elektronisch)978-989-758-402-2
DOIs
PublikationsstatusVeröffentlicht - 2020
Veranstaltung16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications: VISIGRAPP 2021 - Virtuell, Österreich
Dauer: 8 Feb 202110 Feb 2021

Konferenz

Konferenz16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Land/GebietÖsterreich
OrtVirtuell
Zeitraum8/02/2110/02/21

ASJC Scopus subject areas

  • Maschinelles Sehen und Mustererkennung
  • Angewandte Informatik
  • Computergrafik und computergestütztes Design

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