From Persons to Animals: Transferring Person Re-Identification Methods to a Multi-Species Animal Domain

The task of re-identifying (ReID) a previously seen object from a different camera angle is a rising research topic in the computer vision community. Most work is done in the domain of person re-identification, though. In this paper we present the applicability of two novel datasets for animal ReID on algorithms for person ReID, which is an unexamined topic today. The first dataset consists of images of 376 different wild common toads in many different poses and camera orientations. The second dataset contains smaller amounts of images showing different species of zoo animals. We evaluated a variety of different ReID algorithms on both datasets. The results show that in our setting even previously unseen toads can be re-identified with a Rank-1 score greater than 93% using the already established algorithms of person ReID. Interestingly, we found that a mixed dataset containing different animal species increases the identification performance for the single species task in comparison to a per-species training.