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

• 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
• ISSN: 2076-2615; 2076-2615
• DOI: 10.3390/ani12060692

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.