dc.contributor.author | Zhang, X | en_US |
dc.contributor.author | Chandramouli, K | en_US |
dc.contributor.author | International Conference on Computer Vision Systems | en_US |
dc.contributor.editor | Tzovaras, D | en_US |
dc.contributor.editor | Giakoumis, D | en_US |
dc.contributor.editor | Vincze, M | en_US |
dc.contributor.editor | Argyros, A | en_US |
dc.date.accessioned | 2020-09-11T08:48:02Z | |
dc.date.available | 2019-07-09 | en_US |
dc.date.issued | 2019-09-20 | en_US |
dc.identifier.isbn | 978-3-030-34995-0 | en_US |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/66959 | |
dc.description.abstract | The recent developments in the field of unmanned aerial vehicles (UAV or drones) technology has generated a lot of interdisciplinary applications, ranging from remote surveillance of energy infrastructure, to agriculture. However, in the context of national security, low-cost drone equipment has also been viewed as an easy means to cause destructive effects against national critical infrastructures and civilian population. Addressing the challenge of real-time detection and continuous tracking, this paper proposed presents a holistic architecture consisting of both software and hardware design. The software-based video analytics component leverages upon the advancement of Region based Fully Convolutional Network model for drone detection. The hardware component includes a low-cost sensing equipment powered by Raspberry Pi for controlling the camera platform for continuously tracking the orientation of the drone by streaming the video footage captured from the long-range surveillance camera. The novelty of the proposed framework is twofold namely the detection of the drone in real-time and continuous tracking of the detected drone through controlling the camera platform. The framework relies on the capability of the long-range camera to lock into the drone and subsequently track the drone through space. The analytics processing component utilises the NVIDIA$$\circledR $$ GeForce$$\circledR $$ GTX 1080 with 8 GB GDDR5X GPU. The experimental results of the proposed framework have been validated against real-world threat scenarios simulated for the protection of the national critical infrastructure. | en_US |
dc.format.extent | 713 - 722 | en_US |
dc.publisher | Springer International Publishing | en_US |
dc.title | Critical Infrastructure Security Against Drone Attacks Using Visual Analytics | en_US |
dc.type | Conference Proceeding | |
dc.rights.holder | © 2019 Springer Nature | |
dc.identifier.doi | 10.1007/978-3-030-34995-0_65 | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Published | en_US |
dcterms.dateAccepted | 2019-07-09 | en_US |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |