CzechLynx: A Dataset for Individual Identification and Pose Estimation of the Eurasian Lynx

• URL: http://arxiv.org/abs/2506.04931v1
• Date: Thu, 05 Jun 2025 12:05:43 GMT
• Title: CzechLynx: A Dataset for Individual Identification and Pose Estimation of the Eurasian Lynx
• Authors: Lukas Picek, Elisa Belotti, Michal Bojda, Ludek Bufka, Vojtech Cermak, Martin Dula, Rostislav Dvorak, Luboslav Hrdy, Miroslav Jirik, Vaclav Kocourek, Josefa Krausova, Jirı Labuda, Jakub Straka, Ludek Toman, Vlado Trulık, Martin Vana, Miroslav Kutal,
• Abstract summary: We introduce CzechLynx, the first large-scale, open-access dataset for individual identification, 2D pose estimation, and instance segmentation of the Eurasian lynx (Lynx lynx)<n>CzechLynx includes more than 30k camera trap images annotated with segmentation masks, identity labels, and 20-point skeletons and covers 219 unique individuals across 15 years of systematic monitoring in two geographically distinct regions: Southwest Bohemia and the Western Carpathians.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We introduce CzechLynx, the first large-scale, open-access dataset for individual identification, 2D pose estimation, and instance segmentation of the Eurasian lynx (Lynx lynx). CzechLynx includes more than 30k camera trap images annotated with segmentation masks, identity labels, and 20-point skeletons and covers 219 unique individuals across 15 years of systematic monitoring in two geographically distinct regions: Southwest Bohemia and the Western Carpathians. To increase the data variability, we create a complementary synthetic set with more than 100k photorealistic images generated via a Unity-based pipeline and diffusion-driven text-to-texture modeling, covering diverse environments, poses, and coat-pattern variations. To allow testing generalization across spatial and temporal domains, we define three tailored evaluation protocols/splits: (i) geo-aware, (ii) time-aware open-set, and (iii) time-aware closed-set. This dataset is targeted to be instrumental in benchmarking state-of-the-art models and the development of novel methods for not just individual animal re-identification.

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