  Correlation filter (CF) based trackers have aroused increasing attentions in
visual tracking field due to the superior performance on several datasets while
maintaining high running speed. For each frame, an ideal filter is trained in
order to discriminate the target from its surrounding background. Considering
that the target always undergoes external and internal interference during
tracking procedure, the trained filter should take consideration of not only
the external distractions but also the target appearance variation
synchronously. To this end, we present a State-aware Anti-drift Tracker (SAT)
in this paper, which jointly model the discrimination and reliability
information in filter learning. Specifically, global context patches are
incorporated into filter training stage to better distinguish the target from
backgrounds. Meanwhile, a color-based reliable mask is learned to encourage the
filter to focus on more reliable regions suitable for tracking. We show that
the proposed optimization problem could be efficiently solved using Alternative
Direction Method of Multipliers and fully carried out in Fourier domain.
Extensive experiments are conducted on OTB-100 datasets to compare the SAT
tracker (both hand-crafted feature and CNN feature) with other relevant
state-of-the-art methods. Both quantitative and qualitative evaluations further
demonstrate the effectiveness and robustness of the proposed work.
