Convolutional Neural Network for Crowd Counting on Metro Platforms

Jun Zhang,Zhizhong Wang,Jiaze Liu

doi:10.3390/sym13040703

Abstract

Owing to the increased use of urban rail transit, the flow of passengers on metro platforms tends to increase sharply during peak periods. Monitoring passenger flow in such areas is important for security-related reasons. In this paper, in order to solve the problem of metro platform passenger flow detection, we propose a CNN (convolutional neural network)-based network called the MP (metro platform)-CNN to accurately count people on metro platforms. The proposed method is composed of three major components: a group of convolutional neural networks is used on the front end to extract image features, a multiscale feature extraction module is used to enhance multiscale features, and transposed convolution is used for upsampling to generate a high-quality density map. Currently, existing crowd-counting datasets do not adequately cover all of the challenging situations considered in this study. Therefore, we collected images from surveillance videos of a metro platform to form a dataset containing 627 images, with 9243 annotated heads. The results of the extensive experiments showed that our method performed well on the self-built dataset and the estimation error was minimum. Moreover, the proposed method could compete with other methods on four standard crowd-counting datasets.

Highlights

In accordance with previous studies [12,13,14], we used mean absolute error (MAE) and mean squared error (MSE) as metrics to evaluate the accuracy of the methods in terms of counting members of a crowd: MAE =
N is the number of test samples; Zi and Zi are the estimated and ground-truth crowd numbers corresponding to the ith sample, which is given by the integration of the density map
We propose a novel method to count the number of people in crowds on metro platforms, called the MP-convolutional neural network (CNN)

Summary

Introduction

Owing to the rapid development of urban rail transit, the lines of operation are expanding, passenger flow continues to increase [1], and rail operators face daunting safety-related challenges in this context. Crowd density in metro stations increases sharply in peak periods of travel. As a large crowd gathers at metro stations and passenger flows increase, the risk of stampedes increases. It is important to analyze passenger flow by monitoring videos of the metro platform, analyzing their content, and identifying abnormalities using computer vision and artificial intelligence [2,3]. According to information on real-time passenger flows and crowd densities in different areas, people on a platform can be guided to avoid stampedes, improving the security and efficiency of metro stations

Methods

Results

Conclusion