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PolarBearVidID: A Video-based Re-Identification Benchmark Dataset for Polar Bears

Name: PolarBearVidID: A Video-based Re-Identification Benchmark Dataset for Polar Bears
Keywords: Identity Annotated, Deep Learning, Re-Identification, Animals, Polar Bears

Research datakeyboard_double_arrow_right Dataset 24 Jan 2023Publisher:Zenodo

Authors: Zuerl, Matthias; Dirauf, Richard; Koeferl, Franz; Steinlein, Nils; Sueskind, Jonas; Zanca, Dario; Brehm, Ingrid; +2 Authors

doi: 10.5281/zenodo.7564528 , 10.5281/zenodo.7564529

PolarBearVidID: A Video-based Re-Identification Benchmark Dataset for Polar Bears

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Abstract

The peer-reviewed publication for this dataset has now been published in Animals, an MDPI journal, and can be accessed here: https://doi.org/10.3390/ani13050801. Please cite this when using the dataset. PolarBearVidID includes 13 individual polar bears housed in six institutions. Each identity has 110 sequences on average. The maximum length of the sequences is 8 seconds, respectively 100 frames at a frame rate of 12.5 frames per second. The average length of the sequences is 96.69 images. In total, the dataset includes 1431 sequences. The resolution of the images is set to 256 x 128 pixels. Finally, PolarBearVidID is the first dataset to enable utilizing the movement of a non-human species as a feature for the task of re-identification.

Related Organizations

University of Erlangen-Nuremberg
Germany

Keywords

Identity Annotated, Deep Learning, Re-Identification, Animals, Polar Bears

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