  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.
