  Aerial surveillance and monitoring demand both real-time and robust motion
detection from a moving camera. Most existing techniques for drones involve
sending a video data streams back to a ground station with a high-end desktop
computer or server. These methods share one major drawback: data transmission
is subjected to considerable delay and possible corruption. Onboard computation
can not only overcome the data corruption problem but also increase the range
of motion. Unfortunately, due to limited weight-bearing capacity, equipping
drones with computing hardware of high processing capability is not feasible.
Therefore, developing a motion detection system with real-time performance and
high accuracy for drones with limited computing power is highly desirable. In
this paper, we propose a visual-inertial drone system for real-time motion
detection, namely REDBEE, that helps overcome challenges in shooting scenes
with strong parallax and dynamic background. REDBEE, which can run on the
state-of-the-art commercial low-power application processor (e.g. Snapdragon
Flight board used for our prototype drone), achieves real-time performance with
high detection accuracy. The REDBEE system overcomes obstacles in shooting
scenes with strong parallax through an inertial-aided dual-plane homography
estimation; it solves the issues in shooting scenes with dynamic background by
distinguishing the moving targets through a probabilistic model based on
spatial, temporal, and entropy consistency. The experiments are presented which
demonstrate that our system obtains greater accuracy when detecting moving
targets in outdoor environments than the state-of-the-art real-time onboard
detection systems.
