Abstract
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 this https URL.
Abstract (translated)
无人机（UAV）跟踪的稳健性在许多任务中至关重要，如监视和机器人技术。尽管稳健性非常重要，但很少关注在普通污损下无人机跟踪器的性能。为了解决这个问题，我们提出了UAV-C，一个评估无人机跟踪器在常见污损下的稳健性的大型基准。具体来说，UAV-C基于两个流行的UAV数据集，引入了包括恶意、传感器、模糊和组合污损在内的18种常见污损水平。最后，UAV-C包含超过10K个序列。为了了解现有UAV跟踪器对污损的稳健性，我们详细评估了UAV-C上的12个代表算法。我们的研究揭示了几个关键发现：1）当前的跟踪器对污损非常脆弱，表明需要更多地关注增强UAV跟踪器的稳健性；2）当一起出现时，组合污损会导致跟踪器性能的更严重恶化；3）虽然每个跟踪器都有其独特的性能概况，但有些跟踪器可能对特定污损更加敏感。通过发布UAV-C，我们希望它，再加上全面的分析，成为提高无人机跟踪器对抗污损的有价值的资源。我们的UAV-C将在此链接上可用：https://www.url。
URL
https://arxiv.org/abs/2403.11424
