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

TL;DR

This paper investigates how training set size affects animal re-identification methods, comparing local features and end-to-end learning across multiple species, highlighting species-specific factors influencing data needs.

Contribution

It provides a comprehensive experimental analysis of training data impact on different re-identification methods for various animal species, emphasizing species-specific characteristics.

Findings

End-to-end methods outperform local features with sufficient data

Local feature methods are more practical for species with limited data

Species-specific traits significantly influence training data requirements

Abstract

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.