Detecting animals in African Savanna with UAVs and the crowds
This work addresses wildlife monitoring for conservation management, but it is incremental as it applies existing methods to a new domain with standard equipment.
The paper tackles the problem of detecting large mammals in semi-arid Savanna using UAVs by proposing a semi-automatic system based on machine learning trained with crowd-sourced annotations, achieving a high recall rate that reduces manual effort for false detections.
Unmanned aerial vehicles (UAVs) offer new opportunities for wildlife monitoring, with several advantages over traditional field-based methods. They have readily been used to count birds, marine mammals and large herbivores in different environments, tasks which are routinely performed through manual counting in large collections of images. In this paper, we propose a semi-automatic system able to detect large mammals in semi-arid Savanna. It relies on an animal-detection system based on machine learning, trained with crowd-sourced annotations provided by volunteers who manually interpreted sub-decimeter resolution color images. The system achieves a high recall rate and a human operator can then eliminate false detections with limited effort. Our system provides good perspectives for the development of data-driven management practices in wildlife conservation. It shows that the detection of large mammals in semi-arid Savanna can be approached by processing data provided by standard RGB cameras mounted on affordable fixed wings UAVs.