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Volume: 31 | Article ID: art00008
An autonomous drone surveillance and tracking architecture
  DOI :  10.2352/ISSN.2470-1173.2019.15.AVM-035  Published OnlineJanuary 2019

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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Eren Unlu, Emmaneul Zenou, Nicolas Riviere, Paul-Edouard Dupouy, "An autonomous drone surveillance and tracking architecturein Proc. IS&T Int’l. Symp. on Electronic Imaging: Autonomous Vehicles and Machines Conference,  2019,  pp 35-1 - 35-7,

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