  In this paper, we present a novel method Coarse- and Fine-grained Attention
Network (CFANet) for generating high-quality crowd density maps and people
count estimation by incorporating attention maps to better focus on the crowd
area. We devise a from-coarse-to-fine progressive attention mechanism by
integrating Crowd Region Recognizer (CRR) and Density Level Estimator (DLE)
branch, which can suppress the influence of irrelevant background and assign
attention weights according to the crowd density levels, because generating
accurate fine-grained attention maps directly is normally difficult. We also
employ a multi-level supervision mechanism to assist the backpropagation of
gradient and reduce overfitting. Besides, we propose a Background-aware
Structural Loss (BSL) to reduce the false recognition ratio while improving the
structural similarity to groundtruth. Extensive experiments on commonly used
datasets show that our method can not only outperform previous state-of-the-art
methods in terms of count accuracy but also improve the image quality of
density maps as well as reduce the false recognition ratio.
