  Wildlife camera traps and crowd-sourced image material provide novel
possibilities to monitor endangered animal species. However, massive image
volumes that these methods produce are overwhelming for researchers to go
through manually which calls for automatic systems to perform the analysis. The
analysis task that has gained the most attention is the re-identification of
individuals, as it allows, for example, to study animal migration or to
estimate the population size. The Saimaa ringed seal (Pusa hispida saimensis)
is an endangered subspecies only found in the Lake Saimaa, Finland, and is one
of the few existing freshwater seal species. Ringed seals have permanent pelage
patterns that are unique to each individual which can be used for the
identification of individuals. Large variation in poses further exacerbated by
the deformable nature of seals together with varying appearance and low
contrast between the ring pattern and the rest of the pelage makes the Saimaa
ringed seal re-identification task very challenging, providing a good benchmark
to evaluate state-of-the-art re-identification methods. Therefore, we make our
Saimaa ringed seal image (SealID) dataset (N=57) publicly available for
research purposes. In this paper, the dataset is described, the evaluation
protocol for re-identification methods is proposed, and the results for two
baseline methods HotSpotter and NORPPA are provided. The SealID dataset has
been made publicly available.
