Vehicle detection in Unmanned Aerial Vehicle (UAV) captured images has wide
applications in aerial photography and remote sensing. There are many public
benchmark datasets proposed for the vehicle detection and tracking in UAV
images. Recent studies show that adding an adversarial patch on objects can
fool the well-trained deep neural networks based object detectors, posing
security concerns to the downstream tasks. However, the current public UAV
datasets might ignore the diverse altitudes, vehicle attributes, fine-grained
instance-level annotation in mostly side view with blurred vehicle roof, so
none of them is good to study the adversarial patch based vehicle detection
attack problem. In this paper, we propose a new dataset named EVD4UAV as an
altitude-sensitive benchmark to evade vehicle detection in UAV with 6,284
images and 90,886 fine-grained annotated vehicles. The EVD4UAV dataset has
diverse altitudes (50m, 70m, 90m), vehicle attributes (color, type),
fine-grained annotation (horizontal and rotated bounding boxes, instance-level
mask) in top view with clear vehicle roof. One white-box and two black-box
patch based attack methods are implemented to attack three classic deep neural
networks based object detectors on EVD4UAV. The experimental results show that
these representative attack methods could not achieve the robust
altitude-insensitive attack performance.

本研究提出了一种新的数据集 EVD4UAV，用于高度敏感的无人机中对车辆进行检测，包含多样的高度、车辆属性和详细的标注信息，并实施了基于白盒和黑盒的补丁攻击方法来攻击三种典型的深度神经网络目标检测器，实验结果表明这些攻击方法无法实现稳定的高度无关的攻击性能。

EVD4UAV: 无人机避开车辆检测的高度敏感基准

EVD4UAV: An Altitude-Sensitive Benchmark to Evade Vehicle Detection in  UAV

The outstanding computational efficiency of discriminative correlation filter
(DCF) fades away with various complicated improvements. Previous appearances
are also gradually forgotten due to the exponential decay of historical views
in traditional appearance updating scheme of DCF framework, reducing the
model's robustness. In this work, a novel tracker based on DCF framework is
proposed to augment memory of previously appeared views while running at
real-time speed. Several historical views and the current view are
simultaneously introduced in training to allow the tracker to adapt to new
appearances as well as memorize previous ones. A novel rapid compressed context
learning is proposed to increase the discriminative ability of the filter
efficiently. Substantial experiments on UAVDT and UAV123 datasets have
validated that the proposed tracker performs competitively against other 26 top
DCF and deep-based trackers with over 40 FPS on CPU.

本研究提出了一种基于判别相关滤波器框架的新型跟踪器，通过引入历史视图并运行实时速度来拓展记忆，通过快速压缩上下文学习来提高滤波器的鉴别能力，并在 UAVDT 和 UAV123 数据集上验证了该跟踪器的竞争性能。