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

Zürl M, Stoll P, Brehm I, Raab R, Zanca D, Kabri S, Happold J, Nille H, Prechtel K, Wünsch S, Krause M, Seegerer S, von Fersen L, Eskofier B (2022)

Publication Type: Journal article

Publication year: 2022

Journal

Animals MDPI

Book Volume: 12

Pages Range: 692

Issue: 6

DOI: 10.3390/ani12060692

Abstract

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.

Authors with CRIS profile

Matthias Zürl Machine Learning and Data Analytics Lab Philip Stoll Machine Learning and Data Analytics Lab Ingrid Brehm Lehrstuhl für Tierphysiologie René Raab Machine Learning and Data Analytics Lab Dario Zanca Machine Learning and Data Analytics Lab Samira Kabri Lehrstuhl für Angewandte Mathematik (Modellierung und Numerik) Johanna Happold Machine Learning and Data Analytics Lab Heiko Nille Machine Learning and Data Analytics Lab Katharina Prechtel
Sophie Wünsch
Björn Eskofier Machine Learning and Data Analytics Lab

Related research project(s)

Video-based Re-Identification for Animals (Team VERA) Jan. 1, 2021 - Jan. 1, 2024

Involved external institutions

Tiergarten Nürnberg DE Germany (DE)

How to cite

APA:

Zürl, M., Stoll, P., Brehm, I., Raab, R., Zanca, D., Kabri, S.,... Eskofier, B. (2022). Automated Video-Based Analysis Framework for Behavior Monitoring of Individual Animals in Zoos Using Deep Learning—A Study on Polar Bears. Animals, 12, 692. https://dx.doi.org/10.3390/ani12060692

MLA:

Zürl, Matthias, et al. "Automated Video-Based Analysis Framework for Behavior Monitoring of Individual Animals in Zoos Using Deep Learning—A Study on Polar Bears." Animals 12 (2022): 692.

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