Towards Automatic Identification of Elephants in the Wild

TL;DR

This paper presents an automated system for identifying individual elephants in the wild using limited training data, combining object localization, CNN features, and SVMs to assist researchers in biodiversity monitoring.

Contribution

The novel system integrates object part localization, CNN features, and multi-image aggregation to improve elephant identification accuracy with few training images.

Findings

Achieved 56% top-1 accuracy on a dataset of 276 elephants.

Improved to 74% top-1 accuracy when using multiple images.

System effectively handles occlusion, viewpoints, and poses.

Abstract

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.