  Background noise and scale variation are common problems that have been long
recognized in crowd counting. Humans glance at a crowd image and instantly know
the approximate number of human and where they are through attention the crowd
regions and the congestion degree of crowd regions with a global receptive
field. Hence, in this paper, we propose a novel feedback network with
Region-Aware block called RANet by modeling humans Top-Down visual perception
mechanism. Firstly, we introduce a feedback architecture to generate priority
maps that provide prior about candidate crowd regions in input images. The
prior enables the RANet pay more attention to crowd regions. Then we design
Region-Aware block that could adaptively encode the contextual information into
input images through global receptive field. More specifically, we scan the
whole input images and its priority maps in the form of column vector to obtain
a relevance matrix estimating their similarity. The relevance matrix obtained
would be utilized to build global relationships between pixels. Our method
outperforms state-of-the-art crowd counting methods on several public datasets.

Computer Vision and Pattern Recognition