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SeaTurtleID2022: A long-span dataset for reliable sea turtle re-identification (2211.10307v4)

Published 18 Nov 2022 in cs.CV

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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References (52)
  1. Beluga ID 2022. https://lila.science/datasets/beluga-id-2022. Accessed: 4-11-2023.
  2. Humpback whale identification. https://www.kaggle.com/competitions/humpback-whale-identification. Accessed: 4-11-2023.
  3. Right whale recognition. https://www.kaggle.com/c/noaa-right-whale-recognition. Accessed: 4-11-2023.
  4. Turtle recall: Conservation challenge. https://zindi.africa/competitions/turtle-recall-conservation-challenge. Accessed: 4-11-2023.
  5. Automatic individual holstein friesian cattle identification via selective local coat pattern matching in RGB-D imagery. In 2016 IEEE International Conference on Image Processing (ICIP), pages 484–488. IEEE, 2016.
  6. Digiface-1M: 1 million digital face images for face recognition. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pages 3526–3535, 2023.
  7. System for elephant ear-pattern knowledge (seek) to identify individual african elephants. Pachyderm, 61:63–77, 2020.
  8. Flukebook: an open-source AI platform for cetacean photo identification. Mammalian Biology, pages 1–19, 2022.
  9. A computer-assisted system for photographic mark–recapture analysis. Methods in Ecology and Evolution, 3(5):813–822, 2012.
  10. Re-identification of zebrafish using metric learning. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, pages 1–11, 2020.
  11. Stability of facial scale patterns on green sea turtles chelonia mydas over time: A validation for the use of a photo-identification method. Journal of Experimental Marine Biology and Ecology, 476:15–21, 2016.
  12. Grad-CAM++: Generalized gradient-based visual explanations for deep convolutional networks. In 2018 IEEE winter conference on applications of computer vision (WACV), pages 839–847. IEEE, 2018.
  13. Hybrid task cascade for instance segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4974–4983, 2019.
  14. MMDetection: Open mmlab detection toolbox and benchmark. arXiv preprint arXiv:1906.07155, 2019.
  15. Per-pixel classification is not all you need for semantic segmentation. Advances in Neural Information Processing Systems, 34:17864–17875, 2021.
  16. Hotspotter–patterned species instance recognition. In 2013 IEEE Workshop on Applications of Computer Vision (WACV), pages 230–237. IEEE, 2013.
  17. Arcface: Additive angular margin loss for deep face recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4690–4699, 2019.
  18. Superpoint: Self-supervised interest point detection and description. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pages 224–236, 2018.
  19. Hotspotter: Using a computer-driven photo-id application to identify sea turtles. Journal of Experimental Marine Biology and Ecology, 535:151490, 2021.
  20. Deep learning-based methods for individual recognition in small birds. Methods in Ecology and Evolution, 11(9):1072–1085, 2020.
  21. Towards self-supervision for video identification of individual holstein-friesian cattle: The cows2021 dataset. arXiv preprint arXiv:2105.01938, 2021.
  22. Computer-assisted recognition of dolphin individuals using dorsal fin pigmentations. In 2016 International Conference on Image and Vision Computing New Zealand (IVCNZ), pages 1–6. IEEE, 2016.
  23. Mask R-CNN. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Oct 2017.
  24. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 770–778, 2016.
  25. In defense of the triplet loss for person re-identification. arXiv preprint arXiv:1703.07737, 2017.
  26. Estimating population size, structure, and residency time for whale sharks rhincodon typus through collaborative photo-identification. Endangered Species Research, 7(1):39–53, 2009.
  27. Labeled faces in the wild: Updates and new reporting procedures. Technical Report UM-CS-2014-003, University of Massachusetts, Amherst, May 2014.
  28. Segment anything. arXiv preprint arXiv:2304.02643, 2023.
  29. ELPephants: A fine-grained dataset for elephant re-identification. In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pages 0–0, 2019.
  30. Atrw: A benchmark for amur tiger re-identification in the wild. In Proceedings of the 28th ACM International Conference on Multimedia, MM ’20, 2020.
  31. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10012–10022, 2021.
  32. Reproductive Longevity of Loggerhead Sea Turtles Nesting in Greece. Chelonian Conservation and Biology, 19(1):133–136, 2020.
  33. Greece. In P. Casale, editor, Sea turtles in the Mediterranean: distribution, threats and conservation priorities, pages 85–113. IUCN, 2010.
  34. Photo identification for sea turtles: Flipper scales more accurate than head scales using aphis. Journal of Experimental Marine Biology and Ecology, 566:151923, 2023.
  35. SealID: Saimaa ringed seal re-identification dataset. Sensors, 22(19), 2022.
  36. Social media reveals consistently disproportionate tourism pressure on a threatened marine vertebrate. Animal Conservation, 24(4):568–579, 2021.
  37. Animal population censusing at scale with citizen science and photographic identification. In AAAI Spring Symposium Series, 2017.
  38. Re-identification of giant sunfish using keypoint matching. In Proceedings of the Northern Lights Deep Learning Workshop, volume 3, 2022.
  39. A SIFT-based software system for the photo-identification of the risso’s dolphin. Ecological Informatics, 50:95–101, 2019.
  40. Can Drosophila melanogaster tell who’s who? PlOS ONE, 13(10):e0205043, 2018.
  41. Investigating the viability of photo-identification as an objective tool to study endangered sea turtle populations. Journal of Experimental Marine Biology and Ecology, 360(2):103–108, 2008.
  42. Long-term photo-id and satellite tracking reveal sex-biased survival linked to movements in an endangered species. Ecology, 11:e03027, 2020.
  43. More aggressive sea turtles win fights over foraging resources independent of body size and years of presence. Animal Behaviour, 190:209–219, 2022.
  44. Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 815–823, 2015.
  45. Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna. Scientific data, 2(1):1–14, 2015.
  46. NDD20: A large-scale few-shot dolphin dataset for coarse and fine-grained categorisation. arXiv preprint arXiv:2005.13359, 2020.
  47. Botswana Predator Conservation Trust. Panthera pardus CSV custom export, 2022.
  48. Automatic individual recognition of japanese macaques (Macaca fuscata) from sequential images. Ethology, 128(5):461–470, 2022.
  49. Perspectives on individual animal identification from biology and computer vision. Integrative and Comparative Biology, 61(3):900–916, 2021.
  50. Extracting identifying contours for African elephants and humpback whales using a learned appearance model. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1276–1285, 2020.
  51. Claire L Witham. Automated face recognition of rhesus macaques. Journal of Neuroscience Methods, 300:157–165, 2018.
  52. Three-D safari: Learning to estimate zebra pose, shape, and texture from images ”in the wild”. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5359–5368, 2019.
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Authors (4)
  1. Lukáš Adam (13 papers)
  2. Vojtěch Čermák (8 papers)
  3. Kostas Papafitsoros (21 papers)
  4. Lukáš Picek (26 papers)
Citations (13)
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