Automatic image-based re-identification of ringed seals

• Main Author: Nepovinnykh, Ekaterina
• Other Authors: Rahtu, Esa, Lappeenrannan-Lahden teknillinen yliopisto LUT, Lappeenranta-Lahti University of Technology LUT, fi=School of Engineering Science|en=School of Engineering Science|, Stewart, Charles, Kälviäinen, Heikki, Eerola, Tuomas
• Format: Doctoral or Postdoctoral Thesis
• Language: English
• Published: Lappeenranta-Lahti University of Technology LUT 2022
• Subjects: machine vision; image processing; animal re-identification; photo-identification; convolutional neural networks; fi=School of Engineering Science; Laskennallinen tekniikka|en=School of Engineering Science; Computational Engineering|; Norppa; Pusa hispida; ringed seal
• Online Access: https://lutpub.lut.fi/handle/10024/164514

Automated wildlife re-identification has attracted increasing attention in recent years as it provides a non-invasive tool to identify and track individual wild animals over time. Animal re-identification, together with access to a large amount of image material through camera traps and crowd-sourcing, provides novel possibilities for animal monitoring and conservation, in particular, when re-identifying individual animals from the images. The Saimaa ringed seal (Pusa hispida saimensis) is an endangered subspecies endemic to Lake Saimaa, Finland, and one of the few existing freshwater seal species. Ladoga ringed seals (Pusa hispida ladogensis) are a sister species of the Saimaa ringed seals that can only be found in Lake Ladoga. Ringed seals have permanent pelage patterns that are unique to each individual seal and can be used to identify any given member of the species. Their large variety of 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 task of re-identifying a ringed seal a challenge, providing a good benchmark to evaluate state-of-the-art re-identification methods. In this study, the task of individual re-identification of the Saimaa and Ladoga ringed seals is solved by matching images based on animal pelage patterns. The general pipeline for the automatic processing of camera trap and handheld images of seals is proposed. The pipeline consists of three main steps: image preprocessing including seal segmentation, extraction of local pelage patterns and re-identification. Multiple approaches for each step are proposed and evaluated. Three metric learning-based frameworks for ringed seal re-identification: SaimaaID, NOvel Ringed seal re-identification by Pelage Pattern Aggregation (NORPPA) and LadogaID are developed. The extensive evaluation of the different methods are performed on a new and challenging Saimaa ringed seals re-identification dataset called SealID. It is shown that a ...