# Towards Automatic Identification of Elephants in the Wild

arXiv: https://arxiv.org/abs/1812.04418 · HTML: https://mlsanity.com/paper/1812.04418/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2018.

## Built on by (most cited first, 10 of 10)

- [Perspectives on individual animal identification from biology and computer vision](https://mlsanity.com/paper/2103.00560/) (2021)
- [Computer Vision for Primate Behavior Analysis in the Wild](https://mlsanity.com/paper/2401.16424/) (2024)
- [ElephantBook: A Semi-Automated Human-in-the-Loop System for Elephant Re-Identification](https://mlsanity.com/paper/2106.15083/) (2021)
- [CatFLW: Cat Facial Landmarks in the Wild Dataset](https://mlsanity.com/paper/2305.04232/) (2023)
- [Deep Learning Pipeline for Automated Visual Moth Monitoring: Insect Localization and Species Classification](https://mlsanity.com/paper/2307.15427/) (2023)
- [Automated Detection of Cat Facial Landmarks](https://mlsanity.com/paper/2310.09793/) (2023)
- [An Individual Identity-Driven Framework for Animal Re-Identification](https://mlsanity.com/paper/2410.22927/) (2024)
- [Elephants and Algorithms: A Review of the Current and Future Role of AI in Elephant Monitoring](https://mlsanity.com/paper/2306.13803/) (2023)
- [Automated Visual Monitoring of Nocturnal Insects with Light-based Camera Traps](https://mlsanity.com/paper/2307.15433/) (2023)
- [Deep Learning for Leopard Individual Identification: An Adaptive Angular Margin Approach](https://mlsanity.com/paper/2411.01962/) (2024)

## Abstract (arXiv)

Identifying animals from a large group of possible individuals is very important for biodiversity monitoring and especially for collecting data on a small number of particularly interesting individuals, as these have to be identified first before this can be done. Identifying them can be a very time-consuming task. This is especially true, if the animals look very similar and have only a small number of distinctive features, like elephants do. In most cases the animals stay at one place only for a short period of time during which the animal needs to be identified for knowing whether it is important to collect new data on it. For this reason, a system supporting the researchers in identifying elephants to speed up this process would be of great benefit. In this paper, we present such a system for identifying elephants in the face of a large number of individuals with only few training images per individual. For that purpose, we combine object part localization, off-the-shelf CNN features, and support vector machine classification to provide field researches with proposals of possible individuals given new images of an elephant. The performance of our system is demonstrated on a dataset comprising a total of 2078 images of 276 individual elephants, where we achieve 56% top-1 test accuracy and 80% top-10 accuracy. To deal with occlusion, varying viewpoints, and different poses present in the dataset, we furthermore enable the analysts to provide the system with multiple images of the same elephant to be identified and aggregate confidence values generated by the classifier. With that, our system achieves a top-1 accuracy of 74% and a top-10 accuracy of 88% on the held-out test dataset.