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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 %.
Original languageEnglish
Title of host publication Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
EditorsGiovanni Maria Farinella, Petia Radeva, Jose Braz
PublisherSciTePress
Pages370-377
Number of pages8
Volume5, VISAPP
ISBN (Electronic)978-989-758-402-2
DOIs
Publication statusPublished - 2020
Event16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications: VISIGRAPP 2021 - Virtuell, Austria
Duration: 8 Feb 202110 Feb 2021

Conference

Conference16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
CountryAustria
CityVirtuell
Period8/02/2110/02/21

Keywords

  • Convolutional Neural Network
  • Deep Learning
  • Detection
  • Environmental Monitoring
  • Implicit Segmentation

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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