Single object tracking (SOT) is a fundamental problem in computer vision, with a wide range of applications, including autonomous driving, augmented reality, and robot navigation. The robustness of SOT faces two main challenges: tiny target and fast motion. These challenges are especially manifested in videos captured by unmanned aerial vehicles (UAV), where the target is usually far away from the camera and often with significant motion relative to the camera. To evaluate the robustness of SOT methods, we propose BioDrone -- the first bionic drone-based visual benchmark for SOT. Unlike existing UAV datasets, BioDrone features videos captured from a flapping-wing UAV system with a major camera shake due to its aerodynamics. BioDrone hence highlights the tracking of tiny targets with drastic changes between consecutive frames, providing a new robust vision benchmark for SOT. To date, BioDrone offers the largest UAV-based SOT benchmark with high-quality fine-grained manual annotations and automatically generates frame-level labels, designed for robust vision analyses. Leveraging our proposed BioDrone, we conduct a systematic evaluation of existing SOT methods, comparing the performance of 20 representative models and studying novel means of optimizing a SOTA method (KeepTrack KeepTrack) for robust SOT. Our evaluation leads to new baselines and insights for robust SOT. Moving forward, we hope that BioDrone will not only serve as a high-quality benchmark for robust SOT, but also invite future research into robust computer vision. The database, toolkits, evaluation server, and baseline results are available at http://biodrone.aitestunion.com.

翻译：单目标跟踪（SOT）是计算机视觉中的基本问题，广泛应用于自动驾驶、增强现实和机器人导航等领域。SOT的鲁棒性面临两大挑战：小目标与快速运动。在无人机（UAV）拍摄的视频中，这些挑战尤为突出——目标通常距离相机较远，且常与相机存在显著相对运动。为评估SOT方法的鲁棒性，我们提出了BioDrone——首个基于仿生无人机的SOT视觉基准。与现有无人机数据集不同，BioDrone采用扑翼无人机系统采集视频，其空气动力学特性导致相机大幅抖动。因此，BioDrone重点关注相邻帧间剧烈变化的小目标跟踪，为SOT提供了新的鲁棒视觉基准。截至目前，BioDrone是最大的基于无人机的SOT基准数据集，包含高质量细粒度人工标注，并自动生成帧级标签，专为鲁棒视觉分析设计。基于所提出的BioDrone，我们对现有SOT方法进行了系统评估，比较了20个代表性模型的性能，并探索了优化鲁棒SOT前沿方法（KeepTrack）的创新途径。本评估为鲁棒SOT建立了新基线并提供了新见解。展望未来，我们希望BioDrone不仅能作为鲁棒SOT的高质量基准，更能推动未来鲁棒计算机视觉的研究。数据库、工具包、评估服务器及基线结果可在http://biodrone.aitestunion.com获取。