ATG-PVD: Ticketing Parking Violations on A Drone

• Published in: arXiv.org
• Main Authors: Wang, Hengli, Liu, Yuxuan, Huang, Huaiyang, Pan, Yuheng, Yu, Wenbin, Jiang, Jialin, Lyu, Dianbin, Bocus, Mohammud J, Liu, Ming, Pitas, Ioannis, Fan, Rui
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 21.08.2020
• Subjects: Artificial neural networks; Computer Science - Computer Vision and Pattern Recognition; Computer Science - Robotics; Modules; Optical flow (image analysis); Parking; Parking violations; Robotics
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2008.09305

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.