Citation

Wu S, Zhang X, Fang Y. China Saf. Sci. J. 2022; 32(1): 127-134.

Copyright

(Copyright © 2022, China Occupational Safety and Health Association, Publisher Gai Xue bao)

DOI

10.16265/j.cnki.issn1003-3033.2022.01.017

PMID

unavailable

Abstract

In order to accurately predict crowd count in a fixed scene, in the field of crowd analysis, a convolutional neural network (CNN) integrating attention mechanism was adopted, which combined spatial domain attention and channel domain attention. The former could encode pixel-level context information of the entire image to express pixel-level density map more accurately, while the latter could extract more distinguishing features in different channels to significantly express local area of the crowd. Through tests on multiple public data sets, it is found that the crowd counting method based on attention mechanism can accurately estimate number of people in crowded scenes, and it proves better than CSRNet in terms of mean absolute error and mean square error. © 2022, Editorial Department of China Safety Science Journal. All rights reserved.

Language: zh