  Unmanned aerial vehicle (UAV) tracking is crucial for autonomous navigation
and has broad applications in robotic automation fields. However, reliable UAV
tracking remains a challenging task due to various difficulties like frequent
occlusion and aspect ratio change. Additionally, most of the existing work
mainly focuses on explicit information to improve tracking performance,
ignoring potential interframe connections. To address the above issues, this
work proposes a novel framework with continuity-aware latent interframe
information mining for reliable UAV tracking, i.e., ClimRT. Specifically, a new
efficient continuity-aware latent interframe information mining network
(ClimNet) is proposed for UAV tracking, which can generate highly-effective
latent frame between two adjacent frames. Besides, a novel location-continuity
Transformer (LCT) is designed to fully explore continuity-aware
spatial-temporal information, thereby markedly enhancing UAV tracking.
Extensive qualitative and quantitative experiments on three authoritative
aerial benchmarks strongly validate the robustness and reliability of ClimRT in
UAV tracking performance. Furthermore, real-world tests on the aerial platform
validate its practicability and effectiveness. The code and demo materials are
released at https://github.com/vision4robotics/ClimRT.
