Learning To Capture A Film-Look Video With A Camera Drone

The development of intelligent drones has simplified aerial filming and provided smarter assistant tools for users to capture a film-look footage. Existing methods of autonomous aerial filming either specify predefined camera movements for a drone to capture a footage, or employ heuristic approaches for camera motion planning. However, both predefined movements and heuristically planned motions are hardly able to provide cinematic footages for various dynamic scenarios. In this paper, we propose a data-driven learning-based approach, which can imitate a professional cameraman's intention for capturing a film-look aerial footage of a single subject in real-time. We model the decision-making process of the cameraman with two steps: 1) we train a network to predict the future image composition and camera position, and 2) our system then generates control commands to achieve the desired shot framing. At the system level, we deploy our algorithm on the limited resources of a drone and demonstrate the feasibility of running automatic filming onboard in real-time. Our experiments show how our data-driven planning approach achieves film-look footages and successfully mimics the work of a professional cameraman.