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ATG-PVD: Ticketing Parking Violations on A Drone

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posted on 21.08.2020 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.

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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

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Exports