arXiv 2403.201732024-03-29cs.CV

MCNet：一种基于集成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

College of Mechanical and Electronic Engineering, Dalian Minzu University（大连民族大学机电工程学院）；School of Computer Science and Engineering, Dalian Minzu University（大连民族大学计算机科学与工程学院）；Dalian University of Technology（大连理工大学）；Postdoctoral workstation of Dalian Yongjia Electronic Technology Co., Ltd（大连永佳电子科技有限公司博士后工作站）

Qiang Guo, Rubo Zhang, Di Zhao

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AI总结：

本文提出MCNet地铁人群密度估计网络，通过集成多尺度注意力模块和轻量级纹理特征提取网络，在有限硬件资源下实现高密度、高遮挡场景中的人群密度自动分类。

摘要：

针对地铁视频监控系统尚未能有效解决地铁人群密度估计问题，本文提出了一种地铁人群密度估计网络（称为MCNet），用于自动对乘客的人群密度等级进行分类。首先，提出了一种集成多尺度注意力（IMA）模块，以增强普通分类器提取语义人群纹理特征的能力，从而适应人群纹理特征的特点。IMA模块的创新之处在于融合空洞卷积、多尺度特征提取和注意力机制，以较低的计算成本从更大的感受野中获得多尺度人群特征激活，并增强顶层卷积特征的人群激活状态。其次，提出了一种新颖的轻量级人群纹理特征提取网络，该网络可以直接处理视频帧并自动提取纹理特征用于人群密度估计，同时其更快的图像处理速度和更少的网络参数使其能够灵活部署在硬件资源有限的嵌入式平台上。最后，本文将IMA模块与轻量级人群纹理特征提取网络相结合构建MCNet，并在图像分类数据集Cifar10以及四个人群密度数据集PETS2009、Mall、QUT和SH_METRO上验证该网络的可行性，以验证MCNet是否能够成为地铁视频监控中人群密度估计的合适解决方案，这些场景中存在高密度、高遮挡、透视失真和硬件资源有限等图像处理挑战。

英文摘要：

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.

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