  The understanding of human-object interactions is fundamental in First Person
Vision (FPV). Visual tracking algorithms which follow the objects manipulated
by the camera wearer can provide useful information to effectively model such
interactions. In the last years, the computer vision community has
significantly improved the performance of tracking algorithms for a large
variety of target objects and scenarios. Despite a few previous attempts to
exploit trackers in the FPV domain, a methodical analysis of the performance of
state-of-the-art trackers is still missing. This research gap raises the
question of whether current solutions can be used ``off-the-shelf'' or more
domain-specific investigations should be carried out. This paper aims to
provide answers to such questions. We present the first systematic
investigation of single object tracking in FPV. Our study extensively analyses
the performance of 42 algorithms including generic object trackers and baseline
FPV-specific trackers. The analysis is carried out by focusing on different
aspects of the FPV setting, introducing new performance measures, and in
relation to FPV-specific tasks. The study is made possible through the
introduction of TREK-150, a novel benchmark dataset composed of 150 densely
annotated video sequences. Our results show that object tracking in FPV poses
new challenges to current visual trackers. We highlight the factors causing
such behavior and point out possible research directions. Despite their
difficulties, we prove that trackers bring benefits to FPV downstream tasks
requiring short-term object tracking. We expect that generic object tracking
will gain popularity in FPV as new and FPV-specific methodologies are
investigated.
