  Autonomous landing of Unmanned Aerial Vehicles (UAVs) in crowded scenarios is
crucial for successful deployment of UAVs in populated areas, particularly in
emergency landing situations where the highest priority is to avoid hurting
people. In this work, a new visual-based algorithm for identifying Safe Landing
Zones (SLZ) in crowded scenarios is proposed, considering a camera mounted on
an UAV, where the people in the scene move with unknown dynamics. To do so, a
density map is generated for each image frame using a Deep Neural Network, from
where a binary occupancy map is obtained aiming to overestimate the people's
location for security reasons. Then, the occupancy map is projected to the
head's plane, and the SLZ candidates are obtained as circular regions in the
head's plane with a minimum security radius. Finally, to keep track of the SLZ
candidates, a multiple instance tracking algorithm is implemented using Kalman
Filters along with the Hungarian algorithm for data association. Several
scenarios were studied to prove the validity of the proposed strategy,
including public datasets and real uncontrolled scenarios with people moving in
public squares, taken from an UAV in flight. The study showed promising results
in the search of preventing the UAV from hurting people during emergency
landing.
