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• W3119008923 abstract "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." @default.
• W3119008923 created "2021-01-18" @default.
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• W3119008923 date "2020-01-01" @default.
• W3119008923 modified "2023-10-18" @default.
• W3119008923 title "ATG-PVD: Ticketing Parking Violations on a Drone" @default.
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• W3119008923 doi "https://doi.org/10.1007/978-3-030-66823-5_32" @default.
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