An Autonomous Drone Surveillance and Tracking Architecture

Eren Unlu (1) , Emmaneul Zenou (1) , Nicolas Riviere (2) , Paul-Edouard Dupouy (2)

1 ISAE-SUPAERO - Institut Supérieur de l'Aéronautique et de l'Espace
2 DOTA, ONERA, Université de Toulouse [Toulouse]

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.

Mots clés: Camera; Computer vision; Convolutional neural network; Drone; Error; Image resolution; Infrared device

Domaines: Sciences de l'ingénieur [physics]; Physique [physics]

Eren Unlu, Emmaneul Zenou, Nicolas Riviere, Paul-Edouard Dupouy. An Autonomous Drone Surveillance and Tracking Architecture. 2019 Autonomous Vehicles and Machines Conference, AVM 2019, Jan 2019, Hyatt Regency San Francisco, United States. pp.35-1-35-7, ⟨10.2352/ISSN.2470-1173.2019.15.AVM-035⟩. ⟨hal-02864552⟩