  In recent years, deep learning-based visual object trackers have achieved
state-of-the-art performance on several visual object tracking benchmarks.
However, most tracking benchmarks are focused on ground level videos, whereas
aerial tracking presents a new set of challenges. In this paper, we compare ten
trackers based on deep learning techniques on four aerial datasets. We choose
top performing trackers utilizing different approaches, specifically tracking
by detection, discriminative correlation filters, Siamese networks and
reinforcement learning. In our experiments, we use a subset of OTB2015 dataset
with aerial style videos; the UAV123 dataset without synthetic sequences; the
UAV20L dataset, which contains 20 long sequences; and DTB70 dataset as our
benchmark datasets. We compare the advantages and disadvantages of different
trackers in different tracking situations encountered in aerial data. Our
findings indicate that the trackers perform significantly worse in aerial
datasets compared to standard ground level videos. We attribute this effect to
smaller target size, camera motion, significant camera rotation with respect to
the target, out of view movement, and clutter in the form of occlusions or
similar looking distractors near tracked object.
