Saving Endangered Animals with Astro-Ecology

Conservation science is experiencing an unprecedented challenge in identifying and protecting endangered species across the world. The large stretches of land and sea require innovative solutions for the monitoring of endangered populations. Drones equipped with high resolution cameras with supporting data from satellites have helped to mitigate these challenges. Unfortunately, it is difficult to detect animals from optical images when they might only be a matter of a few pixels across.

By deploying thermal infrared cameras on drones to detect animals from their body heat, they can be detected despite their small size in the images. In the thermal infrared band, animals appear as bright sources on a dark, colder background. Through the use of astronomical source detection techniques, these bright animals can be detected although other warm objects lead to false detections. In this paper we demonstrate a technique which uses modern computer vision to build on astronomical source detection algorithms to create a model for the detection and classification of animal thermal profiles in the presence of other warm objects. Using a dataset from Chester Zoo in the UK, we trained a model using 972 frames from a video of the chimpanzee enclosure and achieved excellent results with a training loss of 0.81 and minimal false detections of warm enviromental sources.