  Siamese trackers perform similarity matching with templates (i.e., target
models) to recursively localize objects within a search region. Several
strategies have been proposed in the literature to update a template based on
the tracker output, typically extracted from the target search region in the
current frame, and thereby mitigate the effects of target drift. However, this
may lead to corrupted templates, limiting the potential benefits of a template
update strategy.
  This paper proposes a model adaptation method for Siamese trackers that uses
a generative model to produce a synthetic template from the object search
regions of several previous frames, rather than directly using the tracker
output. Since the search region encompasses the target, attention from the
search region is used for robust model adaptation. In particular, our approach
relies on an auto-encoder trained through adversarial learning to detect
changes in a target object's appearance and predict a future target template,
using a set of target templates localized from tracker outputs at previous
frames. To prevent template corruption during the update, the proposed tracker
also performs change detection using the generative model to suspend updates
until the tracker stabilizes, and robust matching can resume through dynamic
template fusion.
  Extensive experiments conducted on VOT-16, VOT-17, OTB-50, and OTB-100
datasets highlight the effectiveness of our method, along with the impact of
its key components. Results indicate that our proposed approach can outperform
state-of-art trackers, and its overall robustness allows tracking for a longer
time before failure.
