  Crowd analysis from drones has attracted increasing attention in recent times
due to the ease of use and affordable cost of these devices. However, how this
technology can provide a solution to crowd flow detection is still an
unexplored research question. To this end, we propose a crowd flow detection
method for video sequences shot by a drone. The method is based on a
fully-convolutional network that learns to perform crowd clustering in order to
detect the centroids of crowd-dense areas and track their movement in
consecutive frames. The proposed method proved effective and efficient when
tested on the Crowd Counting datasets of the VisDrone challenge, characterized
by video sequences rather than still images. The encouraging results show that
the proposed method could open up new ways of analyzing high-level crowd
behavior from drones.
