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Aerial surveillance and monitoring demand both real-time and robust motiondetection from a moving camera. Most existing techniques for drones involvesending a video data streams back to a ground station with a high-end desktopcomputer or server. These methods share one major drawback: data transmissionis subjected to considerable delay and possible corruption. Onboard computationcan not only overcome the data corruption problem but also increase the rangeof motion. Unfortunately, due to limited weight-bearing capacity, equippingdrones with computing hardware of high processing capability is not feasible.Therefore, developing a motion detection system with real-time performance andhigh accuracy for drones with limited computing power is highly desirable. Inthis paper, we propose a visual-inertial drone system for real-time motiondetection, namely REDBEE, that helps overcome challenges in shooting sceneswith strong parallax and dynamic background. REDBEE, which can run on thestate-of-the-art commercial low-power application processor (e.g. SnapdragonFlight board used for our prototype drone), achieves real-time performance withhigh detection accuracy. The REDBEE system overcomes obstacles in shootingscenes with strong parallax through an inertial-aided dual-plane homographyestimation; it solves the issues in shooting scenes with dynamic background bydistinguishing the moving targets through a probabilistic model based onspatial, temporal, and entropy consistency. The experiments are presented whichdemonstrate that our system obtains greater accuracy when detecting movingtargets in outdoor environments than the state-of-the-art real-time onboarddetection systems.

REDBEE: A Visual-Inertial Drone System for Real-Time Moving Object Detection