  The crux of long-term tracking lies in the difficulty of tracking the target
with discontinuous moving caused by out-of-view or occlusion. Existing
long-term tracking methods follow two typical strategies. The first strategy
employs a local tracker to perform smooth tracking and uses another re-detector
to detect the target when the target is lost. While it can exploit the temporal
context like historical appearances and locations of the target, a potential
limitation of such strategy is that the local tracker tends to misidentify a
nearby distractor as the target instead of activating the re-detector when the
real target is out of view. The other long-term tracking strategy tracks the
target in the entire image globally instead of local tracking based on the
previous tracking results. Unfortunately, such global tracking strategy cannot
leverage the temporal context effectively. In this work, we combine the
advantages of both strategies: tracking the target in a global view while
exploiting the temporal context. Specifically, we perform global tracking via
ensemble of local trackers spreading the full image. The smooth moving of the
target can be handled steadily by one local tracker. When the local tracker
accidentally loses the target due to suddenly discontinuous moving, another
local tracker close to the target is then activated and can readily take over
the tracking to locate the target. While the activated local tracker performs
tracking locally by leveraging the temporal context, the ensemble of local
trackers renders our model the global view for tracking. Extensive experiments
on six datasets demonstrate that our method performs favorably against
state-of-the-art algorithms.
