Recently, there are lots of tracking methods proposed to improve the performance of visual tracking in videos with challenging situations, such as background clutter, severe occlusion, rotation, and so on. In real unmanned aerial vehicle (UAV) based tracking systems, there are various noises occurring during video capturing, transmission, and processing. However, most existing studies pay attention to improve the robustness and accuracy of visual tracking while ignoring the performance of tracking methods on videos with noise. In this paper, we investigate the performance evaluation of existing tracking methods on videos with noise. A group of noisy UAV based tracking video datasets are constructed and used to the benchmark datasets for analysis of tracking methods. Furthermore, we propose an algorithm for robustness tracking in noisy videos. The performance of 9 tracking methods is evaluated on the proposed dataset. We provide the detailed analysis and discussion on the robustness analysis of different tracking methods on videos with different variance of noises. Our investigation shows that it is still challenging for effective tracking for existing methods on videos with noise. And our proposed method shows promising results in noisy videos.

近年来，提出了许多跟踪方法，以改善具有挑战性情况（例如背景杂波，严重遮挡，旋转等）的视频中的视觉跟踪性能。在基于真实无人机（UAV）的跟踪系统中，在视频捕获，传输和处理过程中会发生各种噪声。但是，大多数现有研究都关注提高视觉跟踪的鲁棒性和准确性，而忽略了对带有噪声的视频使用跟踪方法的性能。在本文中，我们调查了现有跟踪方法对有噪声视频的性能评估。构造了一组基于无声无人机的跟踪视频数据集，并将其用于基准数据集以分析跟踪方法。此外，我们提出了一种在嘈杂视频中进行鲁棒性跟踪的算法。在提出的数据集上评估了9种跟踪方法的性能。我们提供了关于不同跟踪方法对具有不同噪声方差的视频进行鲁棒性分析的详细分析和讨论。我们的调查表明，对于有噪声的视频有效跟踪现有方法仍然存在挑战。我们提出的方法在嘈杂的视频中显示出令人鼓舞的结果。