  Vision-based object tracking has boosted extensive autonomous applications
for unmanned aerial vehicles (UAVs). However, the dynamic changes in flight
maneuver and viewpoint encountered in UAV tracking pose significant
difficulties, e.g. , aspect ratio change, and scale variation. The conventional
cross-correlation operation, while commonly used, has limitations in
effectively capturing perceptual similarity and incorporates extraneous
background information. To mitigate these limitations, this work presents a
novel saliency-guided dynamic vision Transformer (SGDViT) for UAV tracking. The
proposed method designs a new task-specific object saliency mining network to
refine the cross-correlation operation and effectively discriminate foreground
and background information. Additionally, a saliency adaptation embedding
operation dynamically generates tokens based on initial saliency, thereby
reducing the computational complexity of the Transformer architecture. Finally,
a lightweight saliency filtering Transformer further refines saliency
information and increases the focus on appearance information. The efficacy and
robustness of the proposed approach have been thoroughly assessed through
experiments on three widely-used UAV tracking benchmarks and real-world
scenarios, with results demonstrating its superiority. The source code and demo
videos are available at https://github.com/vision4robotics/SGDViT.
