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Open Access An autonomous drone surveillance and tracking architecture

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In this work, we present a computer vision and machine learning backed autonomous drone surveillance system, in order to protect critical locations. The system is composed of a wide angle, high resolution daylight camera and a relatively narrow angle thermal camera mounted on a rotating turret. The wide angle daylight camera allows the detection of flying intruders, as small as 20 pixels with a very low false alarm rate. The primary detection is based on YOLO convolutional neural network (CNN) rather than conventional background subtraction algorithms due its low false alarm rate performance. At the same time, the tracked flying objects are tracked by the rotating turret and classified by the narrow angle, zoomed thermal camera, where classification algorithm is also based on CNNs. The training of the algorithms is performed by artificial and augmented datasets due to scarcity of infrared videos of drones.
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Keywords: Autonomous Surveillance; Convolutional Neural Networks; Counter-Drone system; Video Surveillance

Document Type: Research Article

Publication date: January 13, 2019

This article was made available online on January 13, 2019 as a Fast Track article with title: "An autonomous drone surveillance and tracking architecture".

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  • For more than 30 years, the Electronic Imaging Symposium has been serving those in the broad community - from academia and industry - who work on imaging science and digital technologies. The breadth of the Symposium covers the entire imaging science ecosystem, from capture (sensors, camera) through image processing (image quality, color and appearance) to how we and our surrogate machines see and interpret images. Applications covered include augmented reality, autonomous vehicles, machine vision, data analysis, digital and mobile photography, security, virtual reality, and human vision. IS&T began sole sponsorship of the meeting in 2016. All papers presented at EIs 20+ conferences are open access.

    Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual paper for details.

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