  Most existing trackers based on deep learning perform tracking in a holistic
strategy, which aims to learn deep representations of the whole target for
localizing the target. It is arduous for such methods to track targets with
various appearance variations. To address this limitation, another type of
methods adopts a part-based tracking strategy which divides the target into
equal patches and tracks all these patches in parallel. The target state is
inferred by summarizing the tracking results of these patches. A potential
limitation of such trackers is that not all patches are equally informative for
tracking. Some patches that are not discriminative may have adverse effects. In
this paper, we propose to track the salient local parts of the target that are
discriminative for tracking. In particular, we propose a fine-grained saliency
mining module to capture the local saliencies. Further, we design a
saliency-association modeling module to associate the captured saliencies
together to learn effective correlation representations between the exemplar
and the search image for state estimation. Extensive experiments on five
diverse datasets demonstrate that the proposed method performs favorably
against state-of-the-art trackers.
