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

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 o...

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Bibliographic Details
Published in:Animals (Basel) Vol. 12; no. 6; p. 692
Main Authors: Zuerl, Matthias, Stoll, Philip, Brehm, Ingrid, Raab, René, Zanca, Dario, Kabri, Samira, Happold, Johanna, Nille, Heiko, Prechtel, Katharina, Wuensch, Sophie, Krause, Marie, Seegerer, Stefan, von Fersen, Lorenzo, Eskofier, Bjoern
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 10-03-2022
MDPI
Subjects:
Animal behavior
animal monitoring
Animal welfare
Animals
Aquatic mammals
Automation
Behavior
behavior observation
Biologists
Biology
Cameras
Coordinate transformations
Datasets
Deep learning
Labor
Laboratories
Localization
Monitoring
object detection
Observation methods
Physiology
Travel patterns
Ursus maritimus
Video data
Zoo animals
Zoos
deep learning
animal welfare
behavior observation
animal behavior
animal monitoring
object detection
Ursus maritimus
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Summary: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 ( ). 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.
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ISSN:2076-2615
2076-2615
DOI:10.3390/ani12060692