About: ATG-PVD: Ticketing Parking Violations on A Drone

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	ATG-PVD: Ticketing Parking Violations on A Drone
Creator	Liu, Ming Liu, Yuxuan Huang, Huaiyang Wang, Hengli Bocus, Mohammud Fan, Rui Jiang, Jialin Lyu, Dianbin Pan, Yuheng Pitas, Ioannis Yu, Wenbin
Source	ArXiv
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.
has issue date	2020-08-21(xsd:dateTime)
has license	arxiv
sha1sum (hex)	32dcc81c4d6130e1ee9711f81418e83b4435fe7c
resource representing a document's title	ATG-PVD: Ticketing Parking Violations on A Drone
resource representing a document's body	covid:32dcc81c4d6130e1ee9711f81418e83b4435fe7c#body_text
is schema:about of	named entity 'demonstrate' named entity 'drone' named entity 'Parking' named entity 'EMBEDDED' named entity 'ACTUAL' named entity 'NOVEL' named entity 'EFFICIENT' named entity 'effectively' named entity 'introduce' named entity 'convolutional neural network' named entity 'CNN' named entity 'receptive fields' named entity 're-localized' named entity 'sparse image' named entity 'Froster' named entity 'object detection' named entity 'parking violation' named entity 'optical flow' named entity 'parking violation' named entity 'parking violation' named entity 'ground truth' named entity 'optical flow' named entity 'FOV' named entity 'IoU' named entity 'VSLAM' named entity 'LPDDR4' named entity 'social distancing' named entity 'Transportation in Los Angeles' named entity 're-localized' named entity 'optical flow' named entity 'plate numbers' named entity 'optical flow' named entity 'optical flow' named entity 'optical flow' named entity 'parked cars' named entity 'drone flies' named entity 'optical flow' named entity 'RPN' named entity 'ground truth' named entity 'vehicle type' named entity 'photometric' named entity 'object detection' named entity 'keyframes' named entity 'CCTVs' named entity 'COVID-19' named entity 'TX2' named entity 'bounding box' named entity 'optical flow' named entity 'field of view' named entity 'FlowNet' named entity 'parking violation' named entity 'optical flow' named entity 'optical flow' named entity 'optical flow' named entity 'VSLAM' named entity 'optical flow' named entity 'optical flow' named entity 'optical flow' named entity 'England' named entity 'ground truth' named entity 'object detection' named entity 'optical flow' named entity 'optical flow' named entity 'optical flow' named entity 'USA Today' named entity 'Coronavirus Disease 2019' named entity 'object detection' named entity 'Philadelphia' named entity 'optical flow' named entity 'optical flow' named entity 'photometric' named entity 'bundle adjustment' named entity 'VSLAM' named entity 'VSLAM' named entity 'convolution' named entity 'optical flow' named entity 'inlier'

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