  Understanding human-object interactions is fundamental in First Person Vision
(FPV). Tracking algorithms which follow the objects manipulated by the camera
wearer can provide useful cues to effectively model such interactions. Despite
a few previous attempts to exploit trackers in FPV applications, a methodical
analysis of the performance of state-of-the-art visual trackers in this domain
is still missing. In this short paper, we provide a recap of the first
systematic study of object tracking in FPV. Our work extensively analyses the
performance of recent and baseline FPV trackers with respect to different
aspects. This is achieved through TREK-150, a novel benchmark dataset composed
of 150 densely annotated video sequences. The results suggest that more
research efforts should be devoted to this problem so that tracking could
benefit FPV tasks. The full version of this paper is available at
arXiv:2108.13665.
