论文标题
基于视觉的反UAV检测和跟踪
Vision-based Anti-UAV Detection and Tracking
论文作者
论文摘要
无人驾驶汽车（UAV）已被广泛用于各个领域，他们对安全和隐私的侵犯引起了社会关注。近年来已经引入了针对无人机的几种检测和跟踪系统，但其中大多数基于射频，雷达和其他媒体。我们假设计算机视野足够成熟，可以检测和跟踪入侵无人机。因此，我们提出了一个可见的光模式数据集，称为达利安技术大学反UAV数据集，简称为Anti-UAV。它包含一个带有10,000张图像的检测数据集和一个带有20个视频的跟踪数据集，其中包括短期和长期序列。所有帧和图像均精确注释。我们使用此数据集训练几种现有的检测算法并评估算法的性能。在我们的跟踪数据集中还测试了几种跟踪方法。此外，我们提出了一种清晰而简单的跟踪算法，并结合了继承检测器高精度的检测。广泛的实验表明，在融合检测后，跟踪性能得到了很大的改进，从而为使用我们的数据集提供了新尝试。数据集和结果可在以下网址公开可用：https：//github.com/wangdongdut/dut/dut-dut-anti-uav
Unmanned aerial vehicles (UAV) have been widely used in various fields, and their invasion of security and privacy has aroused social concern. Several detection and tracking systems for UAVs have been introduced in recent years, but most of them are based on radio frequency, radar, and other media. We assume that the field of computer vision is mature enough to detect and track invading UAVs. Thus we propose a visible light mode dataset called Dalian University of Technology Anti-UAV dataset, DUT Anti-UAV for short. It contains a detection dataset with a total of 10,000 images and a tracking dataset with 20 videos that include short-term and long-term sequences. All frames and images are manually annotated precisely. We use this dataset to train several existing detection algorithms and evaluate the algorithms' performance. Several tracking methods are also tested on our tracking dataset. Furthermore, we propose a clear and simple tracking algorithm combined with detection that inherits the detector's high precision. Extensive experiments show that the tracking performance is improved considerably after fusing detection, thus providing a new attempt at UAV tracking using our dataset.The datasets and results are publicly available at: https://github.com/wangdongdut/DUT-Anti-UAV
