  Most of the existing single object trackers track the target in a unitary
local search window, making them particularly vulnerable to challenging factors
such as heavy occlusions and out-of-view movements. Despite the attempts to
further incorporate global search, prevailing mechanisms that cooperate local
and global search are relatively static, thus are still sub-optimal for
improving tracking performance. By further studying the local and global search
results, we raise a question: can we allow more dynamics for cooperating both
results? In this paper, we propose to introduce more dynamics by devising a
dynamic attention-guided multi-trajectory tracking strategy. In particular, we
construct dynamic appearance model that contains multiple target templates,
each of which provides its own attention for locating the target in the new
frame. Guided by different attention, we maintain diversified tracking results
for the target to build multi-trajectory tracking history, allowing more
candidates to represent the true target trajectory. After spanning the whole
sequence, we introduce a multi-trajectory selection network to find the best
trajectory that delivers improved tracking performance. Extensive experimental
results show that our proposed tracking strategy achieves compelling
performance on various large-scale tracking benchmarks. The project page of
this paper can be found at https://sites.google.com/view/mt-track/.
