Animal Tracking within a Formation of Drones

Abstract

In this study, we develop a distributed system that can be used by unmanned aerial vehicles (UAVs) or drones for single-animal tracking in terrestrial settings. The system involves a video object tracking (VOT) solution and a drone formation. The proposed VOT solution is based on the particle filter (PF) with two measurement providers: a colour image segmentation (CIS) approach and a machine learning (ML) technique. They are switched based on the structural similarity (SSIM) index between the initial and the current target appearances to mitigate the limitation of computational resources of civilian drones, and to ensure good tracking performance. At first, the deep learning object detector You Only Look Once version three (YOLOv3) is used as the second measurement provider. The proposed VOT solution has been tested on wildlife footage recorded by drones (and obtained from an animal behaviour group). The tests demonstrate amongst other results that the proposed VOT solution is more efficient when YOLOv3 is replaced by other methods such as boosting and channel and spatial reliability tracking (CSRT). The results suggest the utility of the proposed VOT solution in single-animal tracking with cooperative drones for wildlife preservation.