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Computational techniques for automated tracking and analysis of fish movement in controlled aquatic environments

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Pinkiewicz, T (2012) Computational techniques for automated tracking and analysis of fish movement in controlled aquatic environments. PhD thesis, University of Tasmania.

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Abstract

This thesis presents research on automated video analysis using computer vision
systems, for analysing �sh movements and behaviours in sea cages and tanks.
Video technology is widely used in aquaculture to observe �sh movements,
however these observations can be subjective and �sh can only be observed for
short periods due to the manual labour required and observer fatigue. In research,
the analysis of video footage is tedious and very time consuming, requiring sampling
to make it feasible. It is therefore desirable to automate such analysis and
provide users with a tool that can gather data about �sh movements in real-time,
continuously, objectively and at a high sampling rate. The aim of this thesis is
to develop and validate computer vision systems to track �sh automatically in
sea cages and tanks.
Three computer vision systems are proposed, one for sea cages and two for
tanks, and they consist of two major stages. The �rst stage extracts �sh images
from complex backgrounds in video footage through the process of segmentation.
The second stage is responsible for tracking multiple detected �sh by associating
newly extracted objects with existing tracks of �sh. The system developed for use
in sea cages tracks �sh for short periods and generates measures of �sh movement
- their average swimming speed and direction. The �rst system used in tanks
tracks a small number of �sh over a long period of time with the purpose of longterm
observation of spatial location and agonistic behaviours between individuals.
The second system used in tanks is based on the one developed for sea cages and
is used to track small groups of �sh, with the purpose of observing groups' spatiotemporal
patterns rather than movements of individuals.
When using the sea cage system, variations in swimming speed and direction
were observed within days and between days. Some of these variations could be
attributed to water current changes due to tides, but no consistent patterns were
observed in relation to time of day or feeding. During the transfer of Atlantic
salmon smolts from a freshwater hatchery to sea cages, a pattern of non-schooling
behaviour during the �rst 3-5 weeks was observed, followed by a sharp transition
to schooling behaviour.
In tanks, tracking of two individuals was possible but maintaining unique
identi�cation of �sh was not completely achieved. When tracking groups of �sh,
the tracking system was able to detect variations in swimming speed, while spatiotemporal
patterns were observed in relation to the demand feeder and the water inlet.
The sea cage system has a potential application in the commercial setting,
where it can be used to develop behavioural pro�les of �sh and act as an alarm
system if unusual behaviours are detected. From the research point of view, use
of these automated systems improves the process of gathering data about �sh
movements, provides a high level of sampling and increases the speed of video
processing, which is currently based on manual observation of �sh movement.
Time can be spent on analysing the data rather than on extracting it from video.
While there is some requirement for data analysis in this system, the bene�ts in
extracting data from the video automatically far outweigh the requirement for
data analysis.

Item Type: Thesis (PhD)
Keywords: target tracking, image and video processing, aquaculture, fish behaviour
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Date Deposited: 17 Aug 2012 04:34
Last Modified: 11 Mar 2016 05:53
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