[1]MENG Yuebo,ZHANG Yalin,WANG Zhou.Crowd counting method based on proportion fusion and multilayer scale-aware[J].CAAI Transactions on Intelligent Systems,2024,19(2):307-315.[doi:10.11992/tis.202208048]
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Crowd counting method based on proportion fusion and multilayer scale-aware

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 2 Page number: 307-315 Column: 学术论文—机器感知与模式识别 Public date: 2024-03-05
Title:
Crowd counting method based on proportion fusion and multilayer scale-aware
Author(s):
MENG Yuebo;  ZHANG Yalin;  WANG Zhou
College of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
Keywords:
crowd density estimation and counting; convolutional neural network; multilayer scale-aware; proportional fusion; local consistency loss; density map regression; multiscale information; dilated convolution
CLC:
TP391
DOI:
10.11992/tis.202208048
Abstract:
To deal with the problems of insufficient multiscale feature acquisition, poor fusion, and insufficient utilization of global features as a result of the changing view angles or distances of crowd images in dense scenes, we propose a crowd counting network based on proportion fusion and multilayer scale-aware. First, the backbone network VGG16 is employed to extract the initial characteristics of the population density. Subsequently, a multilayer scale-aware module is developed to acquire a rich expression of multiscale information from the crowd. Afterward, a proportional fusion strategy is designed to reconstruct the multiscale information based on the feature weights captured by the convolution layer and extract the significant crowd features. Lastly, convolution regression is utilized to regress the density map. Concurrently, a local consistency loss function is proposed, which improves the similarity between the generated density map and the real density map by regionalizing the density map and enhances the counting performance. The results of the experiments on multiple population datasets exhibit that the model proposed here surpasses the existing state-of-the-art methods of population density counting and has good generalization in vehicle counting.
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