论文标题
ATG-PVD：在无人机上票违反停车
ATG-PVD: Ticketing Parking Violations on A Drone
论文作者
论文摘要
在本文中，我们介绍了一个新颖的可疑和投资框架，可以很容易地将其嵌入无人机中，以进行自动停车违规检测（PVD）。我们提出的框架包括：1）Swiftflow，这是一种有效，准确的卷积神经网络（CNN），用于无监督的光流估计； 2）流程RCNN，一种用于CAR检测和分类的流引导的CNN； 3）基于视觉大满贯开发的非法停放汽车（IPC）候选调查模块。提出的框架成功地嵌入了来自ATG机器人技术的无人机中。实验结果表明，首先，我们提出的SwiftFlow优于所有其他最先进的无监督的光流估计方法，从速度和准确性方面；其次，我们所提出的流程RCNN可以有效，有效地检测IPC候选者，其性能比我们的基线网络更快，更快的RCNN。最后，无人机重新定位后，我们的研究模块可以成功验证实际的IPC。
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.
