Abstract
Accurate individual identification is essential for monitoring rare amphibians, yet invasive marking is often unsuitable for critically endangered species. We evaluate state-of-the-art computer-vision methods for photographic re-identification of the Hula painted frog (Latonia nigriventer) using 1,233 ventral images from 191 individuals collected during 2013-2020 capture-recapture surveys. We compare deep local-feature matching in a zero-shot setting with deep global-feature embedding models. The local-feature pipeline achieves 98% top-1 closed-set identification accuracy, outperforming all global-feature models; fine-tuning improves the best global-feature model to 60% top-1 (91% top-10) but remains below local matching. To combine scalability with accuracy, we implement a two-stage workflow in which a fine-tuned global-feature model retrieves a short candidate list that is re-ranked by local-feature matching, reducing end-to-end runtime from 6.5-7.8 hours to ~38 minutes while maintaining ~96% top-1 closed-set accuracy on the labeled dataset. Separation of match scores between same- and different-individual pairs supports thresholding for open-set identification, enabling practical handling of novel individuals. We deploy this pipeline as a web application for routine field use, providing rapid, standardized, non-invasive identification to support conservation monitoring and capture-recapture analyses. Overall, in this species, zero-shot deep local-feature matching outperformed global-feature embedding and provides a strong default for photo-identification.
Abstract (translated)
准确的个体识别对于监测稀有两栖动物至关重要，然而，侵入性标记方法通常不适用于极度濒危物种。我们评估了最先进的计算机视觉方法，用于2013年至2020年间在捕捉和再捕捉调查中收集到的191只霍拉彩蛙（Latonia nigriventer）的1,233张腹部图像的摄影重新识别。我们在零样本设置下比较了深度局部特征匹配与深度全局特征嵌入模型的效果。局部特征管道达到了98%的第一名封闭集识别准确率，优于所有全局特征模型；微调将最佳全局特征模型改进至60%的第一名（91%的前十名），但仍低于局部匹配的表现。为了结合可扩展性和准确性，我们实施了一个两阶段工作流程，在该流程中，一个微调后的全局特征模型检索出一个简短的候选名单，然后通过局部特征匹配重新排序，将端到端运行时间从6.5至7.8小时减少到了大约38分钟，同时保持了标记数据集上的约96%的第一名封闭集准确率。同源个体与不同个体配对之间的匹配得分差异支持阈值设置以进行开放集识别，从而能够处理新出现的个体。我们将此流程部署为网络应用程序供日常现场使用，提供快速、标准化且非侵入性的识别方式来支持保护监测和捕捉再释放分析。总体而言，在该物种中，零样本深度局部特征匹配优于全局特征嵌入，并提供了照片识别的一个强大的默认选项。
URL
https://arxiv.org/abs/2601.08798
