Recent advancements in the automatic re-identification of animal individuals
from images have opened up new possibilities for studying wildlife through
camera traps and citizen science projects. Existing methods leverage distinct
and permanent visual body markings, such as fur patterns or scars, and
typically employ one of two strategies: local features or end-to-end learning.
In this study, we delve into the impact of training set size by conducting
comprehensive experiments across six different methods and five animal species.
While it is well known that end-to-end learning-based methods surpass local
feature-based methods given a sufficient amount of good-quality training data,
the challenge of gathering such datasets for wildlife animals means that local
feature-based methods remain a more practical approach for many species. We
demonstrate the benefits of both local feature and end-to-end learning-based
approaches and show that species-specific characteristics, particularly
intra-individual variance, have a notable effect on training data requirements.

最近自动识别动物个体的方法在相机陷阱和公众科学项目中的应用开启了研究野生动物的新可能性。本研究通过对六种不同方法和五种动物物种的全面实验来深入探讨训练数据集规模对算法性能的影响，结果显示基于局部特征和端到端学习的方法都有优势，但对于野生动物种类来说，基于局部特征的方法更实用，同时特定物种的特性，尤其是个体间差异对训练数据要求有显著影响。