通过脚印识别宠物个体，解决丢失动物难寻问题
PawPrint: Whose Footprints Are These? Identifying Animal Individuals by Their Footprints
- 利用脚印图像构建犬猫个体识别数据集
- 在复杂地表上实现高精度个体识别
- 适合宠物管理与野生动物保护场景
截至2023年，美国66%的家庭拥有宠物，每年近千万只猫狗走失或被盗。传统定位方法如GPS项圈或身份照片存在易脱落、信号中断、依赖人工报告等局限。为此，本文提出PawPrint与PawPrint+两个公开数据集，聚焦犬猫个体级脚印识别。通过对现代深度神经网络（如CNN、Transformer）与经典局部特征方法的全面评测发现，不同底面复杂度与数据量条件下，各类方法表现各异。研究提示未来应融合学习型全局表征与局部描述子，以提升真实环境下的识别可靠性。该非侵入式方案为宠物管理与野生动物保护提供新路径。
原文摘要 · Abstract (English)
In the United States, as of 2023, pet ownership has reached 66% of households and continues to rise annually. This trend underscores the critical need for effective pet identification and monitoring methods, particularly as nearly 10 million cats and dogs are reported stolen or lost each year. However, traditional methods for finding lost animals like GPS tags or ID photos have limitations-they can be removed, face signal issues, and depend on someone finding and reporting the pet. To address these limitations, we introduce PawPrint and PawPrint+, the first publicly available datasets focused on individual-level footprint identification for dogs and cats. Through comprehensive benchmarking of both modern deep neural networks (e.g., CNN, Transformers) and classical local features, we observe varying advantages and drawbacks depending on substrate complexity and data availability. These insights suggest future directions for combining learned global representations with local descriptors to enhance reliability across diverse, real-world conditions. As this approach provides a non-invasive alternative to traditional ID tags, we anticipate promising applications in ethical pet management and wildlife conservation efforts.
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