In this paper, we introduce a novel suspect-and-investigate framework, which can be easily embedded in a drone for automated parking violation detection (PVD). Our proposed framework consists of: 1) SwiftFlow, an efficient and accurate convolutional neural network (CNN) for unsupervised optical flow estimation; 2) Flow-RCNN, a flow-guided CNN for car detection and classification; and 3) an illegally parked car (IPC) candidate investigation module developed based on visual SLAM. The proposed framework was successfully embedded in a drone from ATG Robotics. The experimental results demonstrate that, firstly, our proposed SwiftFlow outperforms all other state-of-the-art unsupervised optical flow estimation approaches in terms of both speed and accuracy; secondly, IPC candidates can be effectively and efficiently detected by our proposed Flow-RCNN, with a better performance than our baseline network, Faster-RCNN; finally, the actual IPCs can be successfully verified by our investigation module after drone re-localization.

本文提出了一种新颖的嫌疑人调查架构，可轻松嵌入无人机中进行自动化停车违规检测。所提出的框架包括SwiftFlow、Flow-RCNN和违规停车车辆候选调查模块，并成功地嵌入了ATG机器人的无人机中。实验结果表明，我们提出的SwiftFlow在速度和精度方面优于所有其他最先进的无监督光流估计方法，Flow-RCNN可以有效地检测IPC并比Faster-RCNN获得更好的性能；在无人机重定位后，我们的调查模块可以成功验证实际的IPC。

ATG-PVD：利用无人机对违规停车进行罚款