Flying object detection for automatic UAV recognition

With the increasing use of unmanned aerial vehicles (UAVs) by consumers, automatic UAV detection systems have become increasingly important for security services. In such a system, video imagery is a core modality for the detection task, because it can cover large areas and is very cost-effective to acquire. Many detection systems consist of two parts: flying object detection and subsequent object classification. In this work, we investigate the suitability of a number of flying object detection approaches for the task of UAV detection based on video data from static and moving cameras. We compare approaches based on image differencing with object proposal detectors which are learned from data. Finally, we classify each detection by a convolutional neural network (CNN) into the classes UAV or clutter. Our approach is evaluated on six sequences of challenging real world data which contain multiple UAVs, birds, and background motion.