  Video surveillance using drones is both convenient and efficient due to the
ease of deployment and unobstructed movement of drones in many scenarios. An
interesting application of drone-based video surveillance is to estimate crowd
densities (both pedestrians and vehicles) in public places. Deep learning using
convolution neural networks (CNNs) is employed for automatic crowd counting and
density estimation using images and videos. However, the performance and
accuracy of such models typically depend upon the model architecture i.e.,
deeper CNN models improve accuracy at the cost of increased inference time. In
this paper, we propose a novel crowd density estimation model for drones
(DroneNet) using Self-organized Operational Neural Networks (Self-ONN).
Self-ONN provides efficient learning capabilities with lower computational
complexity as compared to CNN-based models. We tested our algorithm on two
drone-view public datasets. Our evaluation shows that the proposed DroneNet
shows superior performance on an equivalent CNN-based model.
