Quadrotor drones equipped with high-quality cameras have rapidly raised as novel, cheap, and stable devices for filmmakers. While professional drone pilots can create aesthetically pleasing videos in short time, the smooth—and cinematographic—control of a camera drone remains challenging for most users, despite recent tools that either automate part of the process or enable the manual design of waypoints to create drone trajectories. This article moves a step further by offering high-level control of cinematographic drones for the specific task of framing dynamic targets. We propose techniques to automatically and interactively plan quadrotor drone motions in dynamic three-dimensional (3D) environments while satisfying both cinematographic and physical quadrotor constraints. We first propose the Drone Toric Space , a dedicated camera parameter space with embedded constraints, and derive some intuitive on-screen viewpoint manipulators. Second, we propose a dedicated path planning technique that ensures both that cinematographic properties can be enforced along the path and that the path is physically feasible by a quadrotor drone. At last, we build on the Drone Toric Space and the specific path planning technique to coordinate the motion of multiple drones around dynamic targets. A number of results demonstrate the interactive and automated capacities of our approaches on different use-cases.

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empirical mode decomposition and application to multichannel filtering. Signal Processing, Elsevier, 2011, 91 (12) AbstractEmpirical Mode Decomposition (EMD) is an emerging topic in signal processing research, applied in various practical fields due in particular to its data-driven filter bank properties. In this paper, a novel EMD approach called X-EMD (eXtended-EMD) is proposed, which allows for a straightforward decomposition of mono-and multivariate signals without any change in the core of the algorithm. Qualitative results illustrate the good behavior of the proposed algorithm whatever the signal dimension is. Moreover, a comparative study of X-EMD with classical mono-and multivariate methods is presented and shows its competitiveness. Besides, we show that X-EMD extends the filter bank properties enjoyed by monovariate EMD to the case of multivariate EMD. Finally, a practical application on multi-channel sleep recording is presented.