  The robustness of unmanned aerial vehicle (UAV) tracking is crucial in many
tasks like surveillance and robotics. Despite its importance, little attention
is paid to the performance of UAV trackers under common corruptions due to lack
of a dedicated platform. Addressing this, we propose UAV-C, a large-scale
benchmark for assessing robustness of UAV trackers under common corruptions.
Specifically, UAV-C is built upon two popular UAV datasets by introducing 18
common corruptions from 4 representative categories including adversarial,
sensor, blur, and composite corruptions in different levels. Finally, UAV-C
contains more than 10K sequences. To understand the robustness of existing UAV
trackers against corruptions, we extensively evaluate 12 representative
algorithms on UAV-C. Our study reveals several key findings: 1) Current
trackers are vulnerable to corruptions, indicating more attention needed in
enhancing the robustness of UAV trackers; 2) When accompanying together,
composite corruptions result in more severe degradation to trackers; and 3)
While each tracker has its unique performance profile, some trackers may be
more sensitive to specific corruptions. By releasing UAV-C, we hope it, along
with comprehensive analysis, serves as a valuable resource for advancing the
robustness of UAV tracking against corruption. Our UAV-C will be available at
https://github.com/Xiaoqiong-Liu/UAV-C.
