ATG-PVD: Ticketing Parking Violations on a Drone

Hengli Wang, Yuxuan Liu, Huaiyang Huang, Yuheng Pan, Wenbin Yu, Jialin Jiang, Dianbin Lyu, Mohammud J. Bocus, Ming Liu, Ioannis Pitas, Rui Fan*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

4 Citations (Scopus)
38 Downloads (Pure)


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.
Original languageEnglish
Title of host publicationComputer Vision -- ECCV 2020 Workshops
EditorsAdrien Bartoli, Andrea Fusiello
PublisherSpringer International Publishing AG
Number of pages17
ISBN (Electronic)978-3-030-66823-5
ISBN (Print)978-3-030-66822-8
Publication statusPublished - 3 Jan 2021
EventComputer Vision – ECCV 2020 Workshops - Glasgow, United Kingdom
Duration: 23 Aug 202028 Aug 2020
Conference number: 16

Publication series

NameLecture Notes in Computer Science


ConferenceComputer Vision – ECCV 2020 Workshops
Abbreviated titleECCV2020
Country/TerritoryUnited Kingdom
Internet address


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