  Visual object tracking is among the hardest problems in computer vision, as
trackers have to deal with many challenging circumstances such as illumination
changes, fast motion, occlusion, among others. A tracker is assessed to be good
or not based on its performance on the recent tracking datasets, e.g., VOT2019,
and LaSOT. We argue that while the recent datasets contain large sets of
annotated videos that to some extent provide a large bandwidth for training
data, the hard scenarios such as occlusion and in-plane rotation are still
underrepresented. For trackers to be brought closer to the real-world scenarios
and deployed in safety-critical devices, even the rarest hard scenarios must be
properly addressed. In this paper, we particularly focus on hard occlusion
cases and benchmark the performance of recent state-of-the-art trackers (SOTA)
on them. We created a small-scale dataset containing different categories
within hard occlusions, on which the selected trackers are evaluated. Results
show that hard occlusions remain a very challenging problem for SOTA trackers.
Furthermore, it is observed that tracker performance varies wildly between
different categories of hard occlusions, where a top-performing tracker on one
category performs significantly worse on a different category. The varying
nature of tracker performance based on specific categories suggests that the
common tracker rankings using averaged single performance scores are not
adequate to gauge tracker performance in real-world scenarios.
