Automated Video-Based Analysis Framework for Behavior Monitoring of Individual Animals in Zoos Using Deep Learning—A Study on Polar Bears

The monitoring of animals under human care is a crucial tool for biologists and zookeepers to keep track of the animals’ physical and psychological health. Additionally, it enables the analysis of observed behavioral changes and helps to unravel underlying reasons. Enhancing our understanding of ani...

Full description

Bibliographic Details
Published in:Animals
Main Authors: Matthias Zuerl, Philip Stoll, Ingrid Brehm, René Raab, Dario Zanca, Samira Kabri, Johanna Happold, Heiko Nille, Katharina Prechtel, Sophie Wuensch, Marie Krause, Stefan Seegerer, Lorenzo von Fersen, Bjoern Eskofier
Format: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2022
Subjects:
Online Access:https://doi.org/10.3390/ani12060692
id ftmdpi:oai:mdpi.com:/2076-2615/12/6/692/
record_format openpolar
spelling ftmdpi:oai:mdpi.com:/2076-2615/12/6/692/ 2023-08-20T04:10:17+02:00 Automated Video-Based Analysis Framework for Behavior Monitoring of Individual Animals in Zoos Using Deep Learning—A Study on Polar Bears Matthias Zuerl Philip Stoll Ingrid Brehm René Raab Dario Zanca Samira Kabri Johanna Happold Heiko Nille Katharina Prechtel Sophie Wuensch Marie Krause Stefan Seegerer Lorenzo von Fersen Bjoern Eskofier agris 2022-03-10 application/pdf https://doi.org/10.3390/ani12060692 EN eng Multidisciplinary Digital Publishing Institute Animal Welfare https://dx.doi.org/10.3390/ani12060692 https://creativecommons.org/licenses/by/4.0/ Animals; Volume 12; Issue 6; Pages: 692 animal welfare animal behavior deep learning object detection animal monitoring behavior observation Ursus maritimus Text 2022 ftmdpi https://doi.org/10.3390/ani12060692 2023-08-01T04:24:58Z The monitoring of animals under human care is a crucial tool for biologists and zookeepers to keep track of the animals’ physical and psychological health. Additionally, it enables the analysis of observed behavioral changes and helps to unravel underlying reasons. Enhancing our understanding of animals ensures and improves ex situ animal welfare as well as in situ conservation. However, traditional observation methods are time- and labor-intensive, as they require experts to observe the animals on-site during long and repeated sessions and manually score their behavior. Therefore, the development of automated observation systems would greatly benefit researchers and practitioners in this domain. We propose an automated framework for basic behavior monitoring of individual animals under human care. Raw video data are processed to continuously determine the position of the individuals within the enclosure. The trajectories describing their travel patterns are presented, along with fundamental analysis, through a graphical user interface (GUI). We evaluate the performance of the framework on captive polar bears (Ursus maritimus). We show that the framework can localize and identify individual polar bears with an F1 score of 86.4%. The localization accuracy of the framework is 19.9±7.6 cm, outperforming current manual observation methods. Furthermore, we provide a bounding-box-labeled dataset of the two polar bears housed in Nuremberg Zoo. Text Ursus maritimus MDPI Open Access Publishing Animals 12 6 692
institution Open Polar
collection MDPI Open Access Publishing
op_collection_id ftmdpi
language English
topic animal welfare
animal behavior
deep learning
object detection
animal monitoring
behavior observation
Ursus maritimus
spellingShingle animal welfare
animal behavior
deep learning
object detection
animal monitoring
behavior observation
Ursus maritimus
Matthias Zuerl
Philip Stoll
Ingrid Brehm
René Raab
Dario Zanca
Samira Kabri
Johanna Happold
Heiko Nille
Katharina Prechtel
Sophie Wuensch
Marie Krause
Stefan Seegerer
Lorenzo von Fersen
Bjoern Eskofier
Automated Video-Based Analysis Framework for Behavior Monitoring of Individual Animals in Zoos Using Deep Learning—A Study on Polar Bears
topic_facet animal welfare
animal behavior
deep learning
object detection
animal monitoring
behavior observation
Ursus maritimus
description The monitoring of animals under human care is a crucial tool for biologists and zookeepers to keep track of the animals’ physical and psychological health. Additionally, it enables the analysis of observed behavioral changes and helps to unravel underlying reasons. Enhancing our understanding of animals ensures and improves ex situ animal welfare as well as in situ conservation. However, traditional observation methods are time- and labor-intensive, as they require experts to observe the animals on-site during long and repeated sessions and manually score their behavior. Therefore, the development of automated observation systems would greatly benefit researchers and practitioners in this domain. We propose an automated framework for basic behavior monitoring of individual animals under human care. Raw video data are processed to continuously determine the position of the individuals within the enclosure. The trajectories describing their travel patterns are presented, along with fundamental analysis, through a graphical user interface (GUI). We evaluate the performance of the framework on captive polar bears (Ursus maritimus). We show that the framework can localize and identify individual polar bears with an F1 score of 86.4%. The localization accuracy of the framework is 19.9±7.6 cm, outperforming current manual observation methods. Furthermore, we provide a bounding-box-labeled dataset of the two polar bears housed in Nuremberg Zoo.
format Text
author Matthias Zuerl
Philip Stoll
Ingrid Brehm
René Raab
Dario Zanca
Samira Kabri
Johanna Happold
Heiko Nille
Katharina Prechtel
Sophie Wuensch
Marie Krause
Stefan Seegerer
Lorenzo von Fersen
Bjoern Eskofier
author_facet Matthias Zuerl
Philip Stoll
Ingrid Brehm
René Raab
Dario Zanca
Samira Kabri
Johanna Happold
Heiko Nille
Katharina Prechtel
Sophie Wuensch
Marie Krause
Stefan Seegerer
Lorenzo von Fersen
Bjoern Eskofier
author_sort Matthias Zuerl
title Automated Video-Based Analysis Framework for Behavior Monitoring of Individual Animals in Zoos Using Deep Learning—A Study on Polar Bears
title_short Automated Video-Based Analysis Framework for Behavior Monitoring of Individual Animals in Zoos Using Deep Learning—A Study on Polar Bears
title_full Automated Video-Based Analysis Framework for Behavior Monitoring of Individual Animals in Zoos Using Deep Learning—A Study on Polar Bears
title_fullStr Automated Video-Based Analysis Framework for Behavior Monitoring of Individual Animals in Zoos Using Deep Learning—A Study on Polar Bears
title_full_unstemmed Automated Video-Based Analysis Framework for Behavior Monitoring of Individual Animals in Zoos Using Deep Learning—A Study on Polar Bears
title_sort automated video-based analysis framework for behavior monitoring of individual animals in zoos using deep learning—a study on polar bears
publisher Multidisciplinary Digital Publishing Institute
publishDate 2022
url https://doi.org/10.3390/ani12060692
op_coverage agris
genre Ursus maritimus
genre_facet Ursus maritimus
op_source Animals; Volume 12; Issue 6; Pages: 692
op_relation Animal Welfare
https://dx.doi.org/10.3390/ani12060692
op_rights https://creativecommons.org/licenses/by/4.0/
op_doi https://doi.org/10.3390/ani12060692
container_title Animals
container_volume 12
container_issue 6
container_start_page 692
_version_ 1774724374133211136