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\begin{document}

\title{Deep learning based evaluation of the nightly behaviour of African
ungulates in zoos}



\author[1]{Max Hahn-Klimroth}%
\author[1]{Tobias Kapetanopoulos}%
\author[1]{Jennifer Gübert}%
\author[1]{Paul W. Dierkes}%
\affil[1]{Goethe University Frankfurt}%


\vspace{-1em}



  
  \date{May 25, 2024}


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\begin{abstract}
1. The description and analysis of animal behaviour over long periods of
time is one of the most important challenges in ecology. However, most
of these studies are limited due to the time and cost required by human
observers. The collection of data via video recordings allows
observation periods to be extended. However, their evaluation by human
observers is very time-consuming. Progress in automated evaluation,
using suitable deep learning methods, seems to be a forwardlooking
approach to analyse even large amounts of video data in an adequate time
frame. 2. In this study we present amulti-step convolutional neural
network system for detecting animal behaviour states, which works with
high accuracy. An important aspect of our approach is the introduction
of model averaging and post-processing rules to make the system robust
to outliers. 3. Our trained system achieves an in-domain classification
accuracy of \textgreater{}0.92, which is improved to \textgreater{}0.96
by a postprocessing step. In addition, the whole system performs even
well in an out-of-domain classification task with two unknown types,
achieving an average accuracy of 0.93. We provide our system at
https://github.com/Klimroth/Video-Action-Classifier-for-African-Ungulates-in-Zoos/tree/main/mrcnn\_based
so that interested users can train their own models to classify images
and conduct behavioural studies of wildlife. 4. The use of a multi-step
convolutional neural network for fast and accurate classification of
wildlife behaviour facilitates the evaluation of large amounts of image
data in ecological studies and reduces the effort of manual analysis of
images to a high degree. Our system also shows that post-processing
rules are a suitable way to make species-specific adjustments and
substantially increase the accuracy of the description of single
behavioural phases (number, duration). The results in the out-of-domain
classification strongly suggest that our system is robust and achieves a
high degree of accuracy even for new species, so that other settings
(e.g. field studies) can be considered.%
\end{abstract}%



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