WhoAmI: An Automatic Tool for Visual Recognition of Tiger and Leopard Individuals in the Wild
This addresses the prohibitive costs and delays in wildlife conservation and ecology by automating individual recognition, though it is incremental as it applies existing deep learning methods to a new domain.
The paper tackles the problem of manually analyzing millions of camera-trap images for wildlife monitoring by developing an automatic tool that detects animals, identifies species, and recognizes individual tigers and leopards based on body markings, achieving effectiveness on a dataset from Southern India.
Photographs of wild animals in their natural habitats can be recorded unobtrusively via cameras that are triggered by motion nearby. The installation of such camera traps is becoming increasingly common across the world. Although this is a convenient source of invaluable data for biologists, ecologists and conservationists, the arduous task of poring through potentially millions of pictures each season introduces prohibitive costs and frustrating delays. We develop automatic algorithms that are able to detect animals, identify the species of animals and to recognize individual animals for two species. we propose the first fully-automatic tool that can recognize specific individuals of leopard and tiger due to their characteristic body markings. We adopt a class of supervised learning approach of machine learning where a Deep Convolutional Neural Network (DCNN) is trained using several instances of manually-labelled images for each of the three classification tasks. We demonstrate the effectiveness of our approach on a data set of camera-trap images recorded in the jungles of Southern India.