Aiming at the metro video surveillance system has not been able to
effectively solve the metro crowd density estimation problem, a Metro Crowd
density estimation Network (called MCNet) is proposed to automatically classify
crowd density level of passengers. Firstly, an Integrating Multi-scale
Attention (IMA) module is proposed to enhance the ability of the plain
classifiers to extract semantic crowd texture features to accommodate to the
characteristics of the crowd texture feature. The innovation of the IMA module
is to fuse the dilation convolution, multiscale feature extraction and
attention mechanism to obtain multi-scale crowd feature activation from a
larger receptive field with lower computational cost, and to strengthen the
crowds activation state of convolutional features in top layers. Secondly, a
novel lightweight crowd texture feature extraction network is proposed, which
can directly process video frames and automatically extract texture features
for crowd density estimation, while its faster image processing speed and fewer
network parameters make it flexible to be deployed on embedded platforms with
limited hardware resources. Finally, this paper integrates IMA module and the
lightweight crowd texture feature extraction network to construct the MCNet,
and validate the feasibility of this network on image classification dataset:
Cifar10 and four crowd density datasets: PETS2009, Mall, QUT and SH_METRO to
validate the MCNet whether can be a suitable solution for crowd density
estimation in metro video surveillance where there are image processing
challenges such as high density, high occlusion, perspective distortion and
limited hardware resources.

提出了一个称为 MCNet 的地铁人群密度估计网络，它可以自动分类乘客的人群密度水平。该网络整合了多尺度注意（IMA）模块和轻量级人群纹理特征提取网络，通过处理视频帧并自动提取纹理特征来进行人群密度估计，适用于地铁视频监控中的人群密度估计问题。

MCNet：一种基于整合多尺度注意力模块的人群密度估计网络

MCNet: A crowd denstity estimation network based on integrating  multiscale attention module

In multichannel speech enhancement, both spectral and spatial information are
vital for discriminating between speech and noise. How to fully exploit these
two types of information and their temporal dynamics remains an interesting
research problem. As a solution to this problem, this paper proposes a
multi-cue fusion network named McNet, which cascades four modules to
respectively exploit the full-band spatial, narrow-band spatial, sub-band
spectral, and full-band spectral information. Experiments show that each module
in the proposed network has its unique contribution and, as a whole, notably
outperforms other state-of-the-art methods.

本文提出了一种名为 McNet 的多提示融合网络，该网络级联四个模块分别利用全频带空间、窄带空间、子带谱和全频带谱信息，通过实验证明该网络的每个模块都具有其独特的贡献，整体而言显著优于其他最先进的方法。