  The use of drones for aerial cinematography has revolutionized several
applications and industries that require live and dynamic camera viewpoints
such as entertainment, sports, and security. However, safely controlling a
drone while filming a moving target usually requires multiple expert human
operators; hence the need for an autonomous cinematographer. Current approaches
have severe real-life limitations such as requiring fully scripted scenes,
high-precision motion-capture systems or GPS tags to localize targets, and
prior maps of the environment to avoid obstacles and plan for occlusion.
  In this work, we overcome such limitations and propose a complete system for
aerial cinematography that combines: (1) a vision-based algorithm for target
localization; (2) a real-time incremental 3D signed-distance map algorithm for
occlusion and safety computation; and (3) a real-time camera motion planner
that optimizes smoothness, collisions, occlusions and artistic guidelines. We
evaluate robustness and real-time performance in series of field experiments
and simulations by tracking dynamic targets moving through unknown,
unstructured environments. Finally, we verify that despite removing previous
limitations, our system achieves state-of-the-art performance. Videos of the
system in action can be seen at https://youtu.be/ZE9MnCVmumc

Robotics;Computer Vision and Pattern Recognition;Machine Learning