Title:	Towards Multi-Species Animal Re-Identification Towards Multi-Species Animal Re-Identification
Authors:	Fruhner, Maik Tapken, Heiko
Citation:	WSCG 2024: full papers proceedings: 32. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 137-146.
Issue Date:	2024
Publisher:	Václav Skala - UNION Agency
Document type:	konferenční příspěvek conferenceObject
URI:	http://hdl.handle.net/11025/57386
ISSN:	2464–4625 (online) 2464–4617 (print)
Keywords:	reidentifikace;zvířata;hluboké učení;počítačové vidění
Keywords in different language:	re-identification;animals;deep learning;computer vision
Abstract in different language:	Animal Re-Identification (ReID) is a computer vision task that aims to retrieve a query individual from a gallery of known identities across different camera perspectives. It is closely related to the well-researched topic of Person ReID, but offers a much broader spectrum of features due to the large number of animal species. This raises research questions regarding domain generalization from persons to animals and across multiple animal species. In this paper, we present research on the adaptation of popular deep learning-based person ReID algorithms to the animal domain as well as their ability to generalize across species. We introduce two novel datasets for animal ReID. The first one contains images of 376 different wild common toads. The second dataset consists of various species of zoo animals. Subsequently, we optimize various ReID models on these datasets, as well as on 20 datasets published by others, with the objective of evaluating the performance of the models in a non-person domain. Our findings indicate that the domain generalization capabilities of OSNet AIN extend beyond the person ReID task, despite its comparatively small size. This enables us to investigate real-time animal ReID on live video data.
Rights:	© Václav Skala - UNION Agency
Appears in Collections:	WSCG 2024: Full Papers Proceedings

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