PolarBearVidID: A Video-Based Re-Identification Benchmark Dataset for Polar Bears
Zürl M, Dirauf R, Köferl F, Steinlein N, Sueskind J, Zanca D, Brehm I, von Fersen L, Eskofier B (2023)
Publication Type: Journal article
Publication year: 2023
Journal
Book Volume: 13
Pages Range: 801
Issue: 5
DOI: 10.3390/ani13050801
Abstract
Automated monitoring systems have become increasingly important for zoological institutions in the study of their animals’ behavior. One crucial processing step for such a system is the re-identification of individuals when using multiple cameras. Deep learning approaches have become the standard methodology for this task. Especially video-based methods promise to achieve a good performance in re-identification, as they can leverage the movement of an animal as an additional feature. This is especially important for applications in zoos, where one has to overcome specific challenges such as changing lighting conditions, occlusions or low image resolutions. However, large amounts of labeled data are needed to train such a deep learning model. We provide an extensively annotated dataset including 13 individual polar bears shown in 1431 sequences, which is an equivalent of 138,363 images. PolarBearVidID is the first video-based re-identification dataset for a non-human species to date. Unlike typical human benchmark re-identification datasets, the polar bears were filmed in a range of unconstrained poses and lighting conditions. Additionally, a video-based re-identification approach is trained and tested on this dataset. The results show that the animals can be identified with a rank-1 accuracy of 96.6%. We thereby show that the movement of individual animals is a characteristic feature and it can be utilized for re-identification.
Authors with CRIS profile
Matthias Zürl
Machine Learning and Data Analytics Lab
Richard Dirauf
Machine Learning and Data Analytics Lab
Franz Köferl
Machine Learning and Data Analytics Lab
Nils Steinlein
Machine Learning and Data Analytics Lab
Dario Zanca
Machine Learning and Data Analytics Lab
Ingrid Brehm
Lehrstuhl für Tierphysiologie
Björn Eskofier
Machine Learning and Data Analytics Lab
Related research project(s)
Involved external institutions
Germany (DE)How to cite
APA:
Zürl, M., Dirauf, R., Köferl, F., Steinlein, N., Sueskind, J., Zanca, D.,... Eskofier, B. (2023). PolarBearVidID: A Video-Based Re-Identification Benchmark Dataset for Polar Bears. Animals, 13, 801. https://dx.doi.org/10.3390/ani13050801
MLA:
Zürl, Matthias, et al. "PolarBearVidID: A Video-Based Re-Identification Benchmark Dataset for Polar Bears." Animals 13 (2023): 801.
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