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Developing an automated analysis of fish migration video using computer vision algorithms
Descriptive
TitleInfo
Title
Developing an automated analysis of fish migration video using computer vision algorithms
Name
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Engdahl
NamePart
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Julia
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Julia Engdahl
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author
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Beaird
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Nicholas
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Nicholas Beaird
Affiliation
Advisory Committee
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chair
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Rutgers University
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degree grantor
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School of Graduate Studies
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school
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Text
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theses
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2020
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2020-10
Language
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English
Abstract
Dams, fishways, and other hydropower structures have been utilized for centuries and have largely been unevaluated regarding their effects on the surrounding fish populations. Conventional methods (seining and angling) used for such evaluation are often costly and time consuming. The aim of this project is to decrease analysis time by automating fish detection with the use of video monitoring and evaluating two computer vision algorithms: background subtraction and machine learning algorithm You Only Look Once (YOLO). We evaluated these algorithms on video data collected from the Island Farm Weir on the Raritan River in New Jersey. Our results indicate that background subtraction models need to be adaptive with respect to time due to the dynamic aquatic environment, and YOLO needs to be trained and tested on the user’s specific case study dataset for optimal results.
Subject
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Topic
Fish ladder
Subject
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Topic
Oceanography
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Title
Rutgers University Electronic Theses and Dissertations
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ETD_11193
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1 online resource (v, 15 pages)
Note
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M.S.
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Includes bibliographical references
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ETD graduate
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School of Graduate Studies Electronic Theses and Dissertations
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rucore10001600001
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NjNbRU
Identifier
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doi:10.7282/t3-yavb-3m08
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Rights
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The author owns the copyright to this work.
RightsHolder
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Name
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Engdahl
GivenName
Julia
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Permission or license
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2020-09-23 16:23:55
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Julia Engdahl
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Affiliation
Rutgers University. School of Graduate Studies
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Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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windows xp
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2020-09-23T15:45:22
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2020-09-23T18:02:47
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