  In the realm of unmanned aerial vehicle (UAV) tracking, Siamese-based
approaches have gained traction due to their optimal balance between efficiency
and precision. However, UAV scenarios often present challenges such as
insufficient sampling resolution, fast motion and small objects with limited
feature information. As a result, temporal context in UAV tracking tasks plays
a pivotal role in target location, overshadowing the target's precise features.
In this paper, we introduce MT-Track, a streamlined and efficient multi-step
temporal modeling framework designed to harness the temporal context from
historical frames for enhanced UAV tracking. This temporal integration occurs
in two steps: correlation map generation and correlation map refinement.
Specifically, we unveil a unique temporal correlation module that dynamically
assesses the interplay between the template and search region features. This
module leverages temporal information to refresh the template feature, yielding
a more precise correlation map. Subsequently, we propose a mutual transformer
module to refine the correlation maps of historical and current frames by
modeling the temporal knowledge in the tracking sequence. This method
significantly trims computational demands compared to the raw transformer. The
compact yet potent nature of our tracking framework ensures commendable
tracking outcomes, particularly in extended tracking scenarios.
