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An autonomous drone surveillance and tracking architecture

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 train-ing of the algorithms is performed by artiﬁcial and augmented datasets due to scarcity of infrared videos of drones.