SealID_seq ...

• Main Author: Nepovinnykh, Ekaterina
• Format: Dataset
• Language: English
• Published: Ekaterina Nepovinnykh 2023
• Subjects: pattern matching; animal identification; animal re-identification; Saimaa ringed seal; computer vision; Computer and information sciences; FOS Computer and information sciences; Tietojenkäsittely ja informaatiotieteet; Data- och informationsvetenskap; Ecology, evolutionary biology; Ekologia, evoluutiobiologia; Ekologi, evolutionsbiologi; ringed seal
• Online Access: https://dx.doi.org/10.23729/a84bf6be-1f2a-4164-b7a0-ef6103152f6f; https://etsin.fairdata.fi/dataset/7ebc91b4-3a96-4b13-b703-9575f1c759e2

Ringed seal image sequences dataset (SealID_seq) provides a challenging identification task. General purpose animal re-identification methods that utilize fur, feather, skin patterns of animals could be evaluated on that dataset. Large variation in illumination, seal poses, limited size of identifiable region, low contrast between the ring pattern and the rest of the pelage, substantial differences between wet and dry fur and low image quality of automatic camera traps all contribute to the difficulty of the re-identification task of Saimaa ringed seals. We have compiled an extensive dataset, consisting of 32077 images in 120 separate sequences, and made it publicly available. All animal individuals were identified by experts. We are making the SealID_seq available to the research community free of charge. If you use this database in your research, we kindly ask that you reference our papers listed below: Nepovinnykh, E., Vilkman, A., Eerola, T. and Kälviäinen, H., 2023. Re-identification of Saimaa ringed ...