Title: Learning a Robust Society of Tracking Parts,
Abstract: Object tracking is an essential task in computer vision that has been studied
since the early days of the field. Being able to follow objects that undergo
different transformations in the video sequence, including changes in scale,
illumination, shape and occlusions, makes the problem extremely difficult. One
of the real challenges is to keep track of the changes in objects appearance
and not drift towards the background clutter. Different from previous
approaches, we obtain robustness against background with a tracker model that
is composed of many different parts. They are classifiers that respond at
different scales and locations. The tracker system functions as a society of
parts, each having its own role and level of credibility. Reliable classifiers
decide the tracker's next move, while newcomers are first monitored before
gaining the necessary level of reliability to participate in the decision
process. Some parts that loose their consistency are rejected, while others
that show consistency for a sufficiently long time are promoted to permanent
roles. The tracker system, as a whole, could also go through different phases,
from the usual, normal functioning to states of weak agreement and even crisis.
The tracker system has different governing rules in each state. What truly
distinguishes our work from others is not necessarily the strength of
individual tracking parts, but the way in which they work together and build a
strong and robust organization. We also propose an efficient way to learn
simultaneously many tracking parts, with a single closed-form formulation. We
obtain a fast and robust tracker with state of the art performance on the
challenging OTB50 dataset.