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