Computer Science > Computer Vision and Pattern Recognition
[Submitted on 18 Nov 2022 (v1), last revised 1 May 2024 (this version, v4)]
Title:SeaTurtleID2022: A long-span dataset for reliable sea turtle re-identification
View PDF HTML (experimental)Abstract:This paper introduces the first public large-scale, long-span dataset with sea turtle photographs captured in the wild -- \href{this https URL}{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\%.
Submission history
From: Kostas Papafitsoros [view email][v1] Fri, 18 Nov 2022 15:46:24 UTC (18,962 KB)
[v2] Mon, 20 Mar 2023 11:30:49 UTC (47,685 KB)
[v3] Thu, 29 Feb 2024 18:11:59 UTC (47,685 KB)
[v4] Wed, 1 May 2024 13:16:09 UTC (15,205 KB)
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