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.

无人机(UAV)跟踪的鲁棒性对于监视和机器人技术等许多任务至关重要。为了解决共同损坏情况下UAV跟踪器性能评估缺乏专门平台的问题，我们提出了UAV-C (UAV跟踪器鲁棒性评估的大规模基准测试集)。具体而言，UAV-C基于两个流行的UAV数据集，引入了来自4个代表性类别（对抗性、传感器、模糊和复合损坏）的18种常见损坏，在不同水平上构建了UAV-C，最终包含了超过10,000个序列。通过在UAV-C上广泛评估12种代表性算法，我们的研究发现：1）当前跟踪器对损坏非常敏感，需要更多关注提高UAV跟踪器的鲁棒性；2）当共同存在时，复合损坏对跟踪器造成更严重的退化；3）每个跟踪器都具有其独特的性能特征，某些跟踪器可能对特定损坏更敏感。通过发布UAV-C，我们希望它与全面的分析一起，成为推动UAV跟踪器抵御损坏鲁棒性的有价值资源。UAV-C将可在此https URL获取。

无人机追踪对常见干扰的稳健性评测