Abstract We describe a demonstrator application that uses a UAV to monitor and detect falls of an at-risk person. The position and state (upright or fallen) of the person are determined with deep-learning-based computer vision, where existing network weights are used for position detection, while for fall detection the last layer is fine-tuned in additional training. A simple visual servoing control strategy keeps the person in view of the drone, and maintains the drone at a set distance from the person. In experiments, falls were reliably detected, and the algorithm was able to successfully track the person indoors.