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

• URL: http://arxiv.org/abs/2211.10307v4
• Date: Wed, 1 May 2024 13:16:09 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 public large-scale, long-span dataset with sea turtle photographs captured in the wild. The dataset contains 8729 photographs of 438 unique individuals collected within 13 years.
• 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 -- \href{https://www.kaggle.com/datasets/wildlifedatasets/seaturtleid2022}{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 \textit{time-aware closed-set} with training, validation, and test data from different days/years, and (ii) a \textit{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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