  Object tracking has been broadly applied in unmanned aerial vehicle (UAV)
tasks in recent years. However, existing algorithms still face difficulties
such as partial occlusion, clutter background, and other challenging visual
factors. Inspired by the cutting-edge attention mechanisms, a novel object
tracking framework is proposed to leverage multi-level visual attention. Three
primary attention, i.e., contextual attention, dimensional attention, and
spatiotemporal attention, are integrated into the training and detection stages
of correlation filter-based tracking pipeline. Therefore, the proposed tracker
is equipped with robust discriminative power against challenging factors while
maintaining high operational efficiency in UAV scenarios. Quantitative and
qualitative experiments on two well-known benchmarks with 173 challenging UAV
video sequences demonstrate the effectiveness of the proposed framework. The
proposed tracking algorithm favorably outperforms 12 state-of-the-art methods,
yielding 4.8% relative gain in UAVDT and 8.2% relative gain in UAV123@10fps
against the baseline tracker while operating at the speed of $\sim$ 28 frames
per second.
