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

Abstract

Simple SummaryEvery institution that keeps animals under human care must ensure animal welfare. To analyze the state of an animal, various measurements can be performed, such as blood analysis or fur condition scoring. They also need to be observed as often as possible to gain further insight into their behavior. Such observations are performed manually in most cases, which makes them very labor- and time-intensive and prevent them from being performed on a continual basis. We present a camera-based framework that provides automated observation of animals. The system detects individual animals and analyzes their locations, walking paths, and activity. We test the framework on the two polar bears of the Nuremberg Zoo.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 cm, outperforming current manual observation methods. Furthermore, we provide a bounding-box-labeled dataset of the two polar bears housed in Nuremberg Zoo.