The goal of integrating drones into the civil airspace requires a technical system which robustly detects, tracks and finally avoids aerial objects. Electro-optical cameras have proven to be an adequate sensor to detect traffic, especially for smaller aircraft, gliders or paragliders. However the very challenging environmental conditions and image artifacts such as lens flares often result in a high number of false detections. Depending on the solar radiation lens flares are very common in

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... l images and hard to distinguish from aerial objects on a collision course due to their similar size, shape, brightness and trajectories. In this paper we present an efficient method to detect lens flares within aerial images based on the position of the sun with respect to the observer. Using the date, time, position and attitude of the observer we predict the lens flare direction within the image. Once the direction is known the position, size and shape of the lens flares are extracted. Experiments show that our approach is able to compensate for errors in the parameters influencing the calculation of the lens flare direction. We further integrate the lens flare detection into an aerial object tracking framework. A detailed evaluation of the framework with and without lens flare filter shows that false tracks due to lens flares are successfully suppressed without degrading the overall tracking system performance.