Automated Identification of Indonesian Rhinos

A proposal for the development and training of an automated rhino identification system from limited camera trap data.

The proposed conservation approach requires expanding the camera trap programs to collect data to study the remaining Indonesian rhino populations and then developing an artificial intelligence system to automatically identify the individual rhino in the camera trap images.

This article will detail the design of the artificial intelligence system.

Camera traps have been used to successfully study the Javan Rhino in Ujung Kulon National Park and have been used in limited numbers on Sumatran Rhino in Mt Leuser and Way Kambas National Parks. However, identifying individual rhino from camera trap images is a time consuming process that can only be performed by staff experienced in recognising rhino. This limits the volume of camera trap images that can be processed and makes it impractical to use camera trap images to identify issues that require rapid responses.

Many options exist for studying wild populations of animals. These include transects, GPS tags or collars, camera traps, and satellite or aerial imagery. Transects are an intensive manual process that are expensive to perform over a multiyear project, GPS tags provide excellent data of individual behaviour but are invasive as they require that rhinos are captured for collaring or tagging, and satellite or aerial imagery is unable to penetrate the dense rainforest canopy.

The limited size of the critically endangered Javan and Sumatran populations makes collecting sufficient data to train an image recognition system challenging. There is an inherent limit on the number of individuals the dataset can contain simply due to the size of the populations. While the challenging environment and probability of finding a member of a small population limits the total number of images than can be collected.

This issue is addressed in the design of the image classification system.

In recent years significant progress has been made on developing machine learning systems for identifying individual animals from images. Some significant examples are presented below.

Based on the research above an artificial intelligence system for automatically identifying Indonesian rhino from camera trap images is proposed. The proposed approach, which is described below, is similar to the ones used in the “Towards Automated Visual Monitoring of Individual Gorillas in the Wild” and “Towards Automatic Identification of Elephants in the Wild” papers. As data is collected the modelling pipeline will be tested and adjusted and alternative approaches will be tested based on new and existing research.

The proposed pipeline.

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