Crowd counting network based on multi-scale convolution and attention mechanism

Fang-jun Luan; Hao-tian Bai; Zhi-li Chen

doi:10.1117/12.2674852

2 May 2023 Crowd counting network based on multi-scale convolution and attention mechanism

Fang-jun Luan, Hao-tian Bai, Zhi-li Chen

Proceedings Volume 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023); 126421U (2023) https://doi.org/10.1117/12.2674852
Event: Second International Conference on Electronic Information Engineering, Big Data and Computer Technology (EIBDCT 2023), 2023, Xishuangbanna, China

Abstract

In order to solve the problem of multi-scale in a single image gathering crowd counting, a new crowd counting network based on the fusion of dilating convolution pyramid and context attention mechanism (DCPCANet) is proposed. With the first ten convolutional layers of VGG16 as the front-end network, an dilated convolutional pyramid fusion attention mechanism module (AMP) is proposed, which is introduced into the three-level upsampling feature fusion module to extract fused multi-scale features, and the AMP module stack is used as the back-end network to capture and fuse multiscale features, The context attention module (CAM) is used to generate the feature map with weight, and high-quality crowd density map is output at the same time. Three mainstream public data sets are adopted, ShanghaiTech PartA,ShanghaiTech PartB,UCF_CC_50. Compared with the previous algorithm, the MAE of the UCF_CC_50 dataset is reduced by 11%, which preliminarily verifies the accuracy and robustness of the model.

Citation Download Citation

Fang-jun Luan, Hao-tian Bai, and Zhi-li Chen "Crowd counting network based on multi-scale convolution and attention mechanism", Proc. SPIE 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023), 126421U (2 May 2023); https://doi.org/10.1117/12.2674852

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KEYWORDS

Convolution

Feature extraction

Amplifiers

Education and training

Feature fusion

Head

Data modeling

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