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ATG-PVD: Ticketing Parking Violations on A Drone
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  • Hengli Wang ,
  • Yuxuan Liu ,
  • Huaiyang Huang ,
  • Yuheng Pan ,
  • Wenbin Yu ,
  • Jialin Jiang ,
  • Dianbin Lyu ,
  • Junaid Bocus ,
  • Ming Liu ,
  • Ioannis Pitas ,
  • Rui Fan
Hengli Wang
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Yuxuan Liu
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Huaiyang Huang
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Yuheng Pan
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Wenbin Yu
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Jialin Jiang
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Dianbin Lyu
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Junaid Bocus
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Ioannis Pitas
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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.