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。

了解训练集大小对动物重新识别的影响

Understanding the Impact of Training Set Size on Animal  Re-identification

Image-based re-identification of animal individuals allows gathering of
information such as migration patterns of the animals over time. This, together
with large image volumes collected using camera traps and crowdsourcing, opens
novel possibilities to study animal populations. For many species, the
re-identification can be done by analyzing the permanent fur, feather, or skin
patterns that are unique to each individual. In this paper, we address the
re-identification by combining two types of pattern similarity metrics: 1)
pattern appearance similarity obtained by pattern feature aggregation and 2)
geometric pattern similarity obtained by analyzing the geometric consistency of
pattern similarities. The proposed combination allows to efficiently utilize
both the local and global pattern features, providing a general
re-identification approach that can be applied to a wide variety of different
pattern types. In the experimental part of the work, we demonstrate that the
method achieves promising re-identification accuracies for Saimaa ringed seals
and whale sharks.

图像基础下的动物个体再识别通过分析动物个体的固有毛皮、羽毛或皮肤图案来研究动物种群的时移模式，本文通过结合两种类型的图案相似度度量（图案特征聚合得到的图案外观相似度和分析图案相似性的几何图案相似度）提出了一种高效利用局部和全局图案特征的再识别方法，适用于多种不同类型的图案。实验部分表明，该方法在洞庭湖环封印和鲸鲨的再识别准确度上表现出了有希望的结果。