Robust drone detection with static VIS and SWIR cameras for day and night counter-UAV
Considerable progress with unmanned aerial vehicles (UAVs) has led to an increasing need for counter-UAV systems to detect present, potentially threatening or misused drones. Therefore, a UAV detection algorithm has been developped recently for day and night operation. Whereas high resolution VIS cameras enable to detect UAVs in daylight in further distances, surveillance at night is performed with a short wave infrared (SWIR) camera. The proposed method is based on temporal median computation, structural adaptive image differencing, codebook-based background learning, local density computation, and shape analysis of foreground structures to perform an improved near range change detection for UAVs. Areas with moving scene parts, like leaves in the wind or driving cars on a street, are recognized to minimize false alarms. This paper presents a significant improvement with respect to some of the most challenging tasks in this field, e.g., increasing the UAV detection sensitivity in front of trees with waving leaves, false alarm minimization, and avoiding the background model update problem. The provided results illustrate the reached performance in a variety of different situations.