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Critical Infrastructure Security Against Drone Attacks Using Visual Analytics

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11754)

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.

Keywords

Intruder drones Surveillance camera Critical infrastructure Deep-learning Drone detection and tracking Raspberry Pi 

Notes

Acknowledgement

This work is partially funded by the European Union Horizon 2020 research and innovation program under grant agreement No. 740898 (DEFENDER IP project) and grant agreement No. 787123 (PERSONA RIA project).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Venaka Media LimitedLondonUK

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