For images with congested scenes, the task of crowd analysis,
including crowd counting and crowd distribution prediction,
becomes very difficult. To address these issues, various
CNN-based approaches have been proposed. However, those
methods usually have a large number of parameters and require
huge computing resources. In this paper, we focus on
low-complexity approaches and propose a lightweight endto-
end network for crowd analysis. Our method utilizes an
effective scale-aware module to extract multi-scale features
and then regresses these features to density maps. The proposed
network is consisted by three parts: multi-scale feature
extraction, density map estimation and density map correction,
and the network which only contains 0.86 M parameters
(Lightweight). According to our experiments, our proposal
obtains a better result than other existing methods on several
testing sequences.