TechRxiv
eccv2020uav.pdf (3.41 MB)

ATG-PVD: Ticketing Parking Violations on A Drone

Download (3.41 MB)
preprint
posted on 21.08.2020, 15:36 by Hengli Wang, Yuxuan Liu, Huaiyang Huang, Yuheng Pan, Wenbin Yu, Jialin Jiang, Dianbin Lyu, Junaid Bocus, Ming Liu, Ioannis Pitas, Rui Fan
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.

History

Email Address of Submitting Author

rui.fan@ieee.org

ORCID of Submitting Author

0000-0003-2593-6596

Submitting Author's Institution

UC San Diego

Submitting Author's Country

United States of America

Licence

Exports