SeaTurtleID2022: A long-span dataset for reliable sea turtle re-identification

• URL: http://arxiv.org/abs/2311.05524v2
• Date: Tue, 30 Apr 2024 14:50:59 GMT
• Title: SeaTurtleID2022: A long-span dataset for reliable sea turtle re-identification
• Authors: Lukáš Adam, Vojtěch Čermák, Kostas Papafitsoros, Lukáš Picek,
• Abstract summary: This paper introduces the first large-scale, long-span dataset with sea turtle photographs captured in the wild -- SeaTurtleID2022. The dataset contains 8729 photographs of 438 unique individuals collected within 13 years. Instead of standard "random" splits, the dataset allows for two realistic and ecologically motivated splits.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper introduces the first public large-scale, long-span dataset with sea turtle photographs captured in the wild -- SeaTurtleID2022 (https://www.kaggle.com/datasets/wildlifedatasets/seaturtleid2022). The dataset contains 8729 photographs of 438 unique individuals collected within 13 years, making it the longest-spanned dataset for animal re-identification. All photographs include various annotations, e.g., identity, encounter timestamp, and body parts segmentation masks. Instead of standard "random" splits, the dataset allows for two realistic and ecologically motivated splits: (i) a time-aware closed-set with training, validation, and test data from different days/years, and (ii) a time-aware open-set with new unknown individuals in test and validation sets. We show that time-aware splits are essential for benchmarking re-identification methods, as random splits lead to performance overestimation. Furthermore, a baseline instance segmentation and re-identification performance over various body parts is provided. Finally, an end-to-end system for sea turtle re-identification is proposed and evaluated. The proposed system based on Hybrid Task Cascade for head instance segmentation and ArcFace-trained feature-extractor achieved an accuracy of 86.8%.

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