For Micro Aerial Vehicles (MAVs), robust obstacle avoidance during flight is a challenging problem because only light weight sensors as monocular cameras can be mounted as pay-load. With monocular cameras depth perception cannot be estimated from a single view, which means that information has to be estimated by combining images from multiple view-points. Here we present a method which focuses only on regions classified as foreground and follow features in the foreground to estimate threat of potential objects being an obstacle in the flight path by depth segmentation and scale estimation. We evaluate results from our approach in an outdoor environment with a small quad-rotor drone and analyze the effect of the differ-ent stages in the image processing pipeline. We are able to determine evident obstacles in front of the drone with high confidence. Robust obstacle-avoidance algorithms as presented in this arti-cle will allow Micro Aerial Vehicles to navigate semi-autonomously in outdoor scenarios.