  Recently, the surge in the adoption of single-stream architectures utilizing
pre-trained ViT backbones represents a promising advancement in the field of
generic visual tracking. By integrating feature extraction and fusion into a
cohesive framework, these architectures offer improved performance, efficiency,
and robustness. However, there has been limited exploration into optimizing
these frameworks for UAV tracking. In this paper, we boost the efficiency of
this framework by tailoring it into an adaptive computation framework that
dynamically exits Transformer blocks for real-time UAV tracking. The motivation
behind this is that tracking tasks with fewer challenges can be adequately
addressed using low-level feature representations. Simpler tasks can often be
handled with less demanding, lower-level features. This approach allows the
model use computational resources more efficiently by focusing on complex tasks
and conserving resources for easier ones. Another significant enhancement
introduced in this paper is the improved effectiveness of ViTs in handling
motion blur, a common issue in UAV tracking caused by the fast movements of
either the UAV, the tracked objects, or both. This is achieved by acquiring
motion blur robust representations through enforcing invariance in the feature
representation of the target with respect to simulated motion blur. The
proposed approach is dubbed BDTrack. Extensive experiments conducted on five
tracking benchmarks validate the effectiveness and versatility of our approach,
establishing it as a cutting-edge solution in real-time UAV tracking. Code is
released at: https://github.com/wuyou3474/BDTrack.
