  State-of-the-art methods for counting people in crowded scenes rely on deep
networks to estimate crowd density in the image plane. While useful for this
purpose, this image-plane density has no immediate physical meaning because it
is subject to perspective distortion. This is a concern in sequences acquired
by drones because the viewpoint changes often. This distortion is usually
handled implicitly by either learning scale-invariant features or estimating
density in patches of different sizes, neither of which accounts for the fact
that scale changes must be consistent over the whole scene.
  In this paper, we explicitly model the scale changes and reason in terms of
people per square-meter. We show that feeding the perspective model to the
network allows us to enforce global scale consistency and that this model can
be obtained on the fly from the drone sensors. In addition, it also enables us
to enforce physically-inspired temporal consistency constraints that do not
have to be learned. This yields an algorithm that outperforms state-of-the-art
methods in inferring crowd density from a moving drone camera especially when
perspective effects are strong.

Computer Vision and Pattern Recognition