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arXiv · 2610.39857

Open-set multi-view seahorse re-identification from underwater photograph sequences

Tristan Fauvel·Sylvie Louisy·Patrick Louisy·Thomas Fauvel
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Abstract

Automated animal re-identification from photograph sequences can facilitate minimally invasive Capture-Mark-Recapture studies at scale. Yet, photographs taken during an observation session can vary greatly in quality, not always providing the necessary information for accurate re-identification. In this paper, we present new methods for taxonomic classification, sex determination, and open-set re-identification from image sequences. We train and evaluate our models on a dataset of 35,914 photographs of 474 individual seahorses observed in the wild. We demonstrate that our approach, by combining information from multiple photographs, yields state-of-the-art performance.

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Tristan Fauvel, Sylvie Louisy, Patrick Louisy, Thomas Fauvel. 2026-10-29. Open-set multi-view seahorse re-identification from underwater photograph sequences. https://arxiv.org/abs/2610.39857

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