Crowd density estimation based on multi scale features fusion network with reverse attention mechanism

Deep learning has made substantial progress in crowd counting, but in practical applications, due to interference factors such as perspective distortion and complex background, the existing methods still have large errors in counting. In response to the above problems, this paper designs a multi-sca...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Netherlands), 2022-09, Vol.52 (11), p.13097-13113
Main Authors: Li, Yong-Chao, Jia, Rui-Sheng, Hu, Ying-Xiang, Han, Dong-Nuo, Sun, Hong-Mei
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Computer Science
Crowd monitoring
Datasets
Density
Distortion
Feature extraction
Interference
Machine learning
Machines
Manufacturing
Mechanical Engineering
Modules
Processes
Sensitivity enhancement
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Summary:Deep learning has made substantial progress in crowd counting, but in practical applications, due to interference factors such as perspective distortion and complex background, the existing methods still have large errors in counting. In response to the above problems, this paper designs a multi-scale feature fusion network (IA-MFFCN) based on the reverse attention mechanism, which maps the image to the crowd density map for counting. The network consists of three parts: feature extraction module, inverse attention module, and back-end module. First, to overcome the problem of perspective distortion, deeper single-column CNNs was designed as a feature extraction module to extract multi-scale feature information and merge them; second, to avoid interference of complex backgrounds, the inverse attention module was designed, through the multi-scale inverse attention mechanism, reducing the influence of noise on counting accuracy. Finally, to generate a high-quality crowd density map, dilation convolution was introduced. Simultaneously, to enhance the sensitivity of the network to crowd counting, a comprehensive loss function based on Euclidean loss and predicted population loss is designed to improve training accuracy, to produce a more accurate density value. Experiments show that compared with the comparison algorithm, the algorithm in this paper has a significant reduction in the mean absolute error (MAE) and mean square error (MSE) on the ShanghaiTech dataset, UCF_CC_50 dataset and WorldExpo`10 dataset.
ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-022-03187-y