首个欧亚猞猁个体识别与姿态估计数据集，支持长期跨区域监测研究。
CzechLynx: A Dataset for Individual Identification and Pose Estimation of the Eurasian Lynx
- 构建39,760张相机陷阱图像，标注个体身份、分割掩码和20点骨骼
- 覆盖15年、319个独特个体，含真实与生成的合成图像数据
- 提供地理与时间感知评估协议，适合生态监控场景的模型测试
我们提出CzechLynx，首个大规模、公开可获取的欧亚猞猁（Lynx lynx）个体识别、姿态估计与实例分割数据集。该数据集包含39,760张相机陷阱图像，附带分割掩码、身份标签及20点骨骼标注，覆盖15年系统监测中两个地理区域（西南波西米亚与西喀尔巴阡山）的319个独特个体。除真实数据外，还提供大量逼真合成图像及基于Unity的生成管道，结合扩散文本到纹理建模，可生成任意规模、涵盖多样环境、姿态与毛色变化的合成数据。为支持在真实生态场景下的系统性评估，定义三种互补评估协议：(i) 地理感知，(ii) 时间感知开集，(iii) 时间感知闭集，覆盖跨区域与长期监测设置。CzechLynx为计算机视觉与机器学习模型在真实生态场景下的鲁棒评估提供了独特且灵活的基准。
原文摘要 · Abstract (English)
We introduce CzechLynx, the first large-scale, open-access dataset for individual identification, pose estimation, and instance segmentation of the Eurasian lynx (Lynx lynx). CzechLynx contains 39,760 camera trap images annotated with segmentation masks, identity labels, and 20-point skeletons and covers 319 unique individuals across 15 years of systematic monitoring in two geographically distinct regions: southwest Bohemia and the Western Carpathians. In addition to the real camera trap data, we provide a large complementary set of photorealistic synthetic images and a Unity-based generation pipeline with diffusion-based text-to-texture modeling, capable of producing arbitrarily large amounts of synthetic data spanning diverse environments, poses, and coat-pattern variations. To enable systematic testing across realistic ecological scenarios, we define three complementary evaluation protocols: (i) geo-aware, (ii) time-aware open-set, and (iii) time-aware closed-set, covering cross-regional and long-term monitoring settings. With the provided resources, CzechLynx offers a unique, flexible benchmark for robust evaluation of computer vision and machine learning models across realistic ecological scenarios.
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