Computer vision applications in wildlife conservation

Abstract

Monitoring of protected areas to curb illegal activities like poaching and animal trafficking is a monumental task. To augment existing manual patrolling efforts, unmanned aerial surveillance using visible and thermal infrared (TIR) cameras is increasingly being adopted. Automated data acquisition has become easier with advances in unmanned aerial vehicles (UAVs) and sensors like TIR cameras, which allow surveillance at night when poaching typically occurs. However, it is still a challenge to accurately and quickly process large amounts of the resulting TIR data. In this paper, we present the first large dataset collected using a TIR camera mounted on a fixed-wing UAV in multiple African protected areas. This dataset includes TIR videos of humans and animals with several challenging scenarios like scale variations, background clutter due to thermal reflections, large camera rotations, and motion blur. We also evaluate various recent approaches for single and multi-object tracking. With the increasing popularity of aerial imagery for monitoring and surveillance purposes, we anticipate this unique dataset to be used to develop and evaluate techniques for object detection, tracking, and domain adaptation for aerial, TIR videos. To this end, we explore the use of Person Re-Identification(ReID) techniques for applicability in multi target multi camera tracking. We find that ReId is best suited for multi camera target reidentification compared to single camera tracking