Multi-scale and multi-column convolutional neural network for crowd density estimation
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Multi-scale and multi-column convolutional neural network for crowd density estimation Lei Chen 1 & Guodong Wang 1 & Guojia Hou 1 Received: 7 January 2020 / Revised: 21 August 2020 / Accepted: 29 September 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
In order to accurately identify objects of different sizes, we propose an efficient MultiScale and Multi-Column Convolutional Neural Network (MSMC) to estimate the crowd density. On the one hand, the ground truth is generated based on the existed label information. On the other hand, the image is fed into our model to find the relationship between the ground truth and the predicted density map. The network is composed of three components: feature extraction, feature fusion and feature regression. First, VGG16 is utilized for faster feature extraction. Second, different sizes layers from VGG16 are fused, which helps the detection of objects with different sizes. Third, we apply multi-channel convolution to further solve the issue of multi-sizes. After the fusion block, the dilated convolution is employed to strengthen the receptive field without increasing the amount of parameters. In the crowd density estimation, the combination of multiple sizes and multiple channels enhances the ability of receiving information, improves the mapping ability of the original image and the density map, and promotes the accuracy of crowd density estimation. In this paper, the test results of the ShanghaiTech Dataset and UCF_CC_50 Dataset are provided in the Experiment section, which shows that the proposed method makes an excellent performance in both accuracy and robustness. Keywords Convolutional neural network . Density map . Multi-channels . Dilated convolution
1 Introduction In some special festivals and special occasions, overcrowded people will encounter unexpected losses. Whether the stampede of the Lantern Festival in Beijing fifteen years ago or
* Guodong Wang [email protected]
1
College of Computer Science and Technology, Qingdao University, Qingdao, People’s Republic of China 266071
Multimedia Tools and Applications
the stampede of religious activities in Ningxia five years ago, the stampede has been existing all the time. Therefore, real-time prediction of crowd density is of great significance in preventing accidents. If the crowd density can be predicted in time, some measures can be taken in advance to avoid the terrible situation. For example, in schools, shopping malls and railway stations, relevant departments evacuate the crowds in advance. And at tourist attractions, visitors can enter the spots in batches under the guidance. Of course, timely prediction of crowd density not only efficiently save human from risks. In the mall, real-time understanding of passenger flow and solve the existing problems can identify potential customers and increase economic benefits. At present, the popularity of monitoring systems provides a large amount of data in the direction of crowd density estimation. At the same time, it also broug
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