  This paper proposes a novel model, named Continuity-Discrimination
Convolutional Neural Network (CD-CNN), for visual object tracking. Existing
state-of-the-art tracking methods do not deal with temporal relationship in
video sequences, which leads to imperfect feature representations. To address
this problem, CD-CNN models temporal appearance continuity based on the idea of
temporal slowness. Mathematically, we prove that, by introducing temporal
appearance continuity into tracking, the upper bound of target appearance
representation error can be sufficiently small with high probability. Further,
in order to alleviate inaccurate target localization and drifting, we propose a
novel notion, object-centroid, to characterize not only objectness but also the
relative position of the target within a given patch. Both temporal appearance
continuity and object-centroid are jointly learned during offline training and
then transferred for online tracking. We evaluate our tracker through extensive
experiments on two challenging benchmarks and show its competitive tracking
performance compared with state-of-the-art trackers.
