  Counting people in dense crowds is a demanding task even for humans. This is
primarily due to the large variability in appearance of people. Often people
are only seen as a bunch of blobs. Occlusions, pose variations and background
clutter further compound the difficulty. In this scenario, identifying a person
requires larger spatial context and semantics of the scene. But the current
state-of-the-art CNN regressors for crowd counting are feedforward and use only
limited spatial context to detect people. They look for local crowd patterns to
regress the crowd density map, resulting in false predictions. Hence, we
propose top-down feedback to correct the initial prediction of the CNN. Our
architecture consists of a bottom-up CNN along with a separate top-down CNN to
generate feedback. The bottom-up network, which regresses the crowd density
map, has two columns of CNN with different receptive fields. Features from
various layers of the bottom-up CNN are fed to the top-down network. The
feedback, thus generated, is applied on the lower layers of the bottom-up
network in the form of multiplicative gating. This masking weighs activations
of the bottom-up network at spatial as well as feature levels to correct the
density prediction. We evaluate the performance of our model on all major crowd
datasets and show the effectiveness of top-down feedback.
