Dense Crowd Counting Algorithm Based on New Multi-scale Attention Mechanism

WAN Honglin; WANG Xiaomin; PENG Zhenwei; BAI Zhiquan; YANG Xinghai; SUN Jiande

doi:10.11999/JEIT210163

Volume 44 Issue 3

Mar. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(3): 1129-1136

WAN Honglin, WANG Xiaomin, PENG Zhenwei, BAI Zhiquan, YANG Xinghai, SUN Jiande. Dense Crowd Counting Algorithm Based on New Multi-scale Attention Mechanism[J]. Journal of Electronics & Information Technology, 2022, 44(3): 1129-1136. doi: 10.11999/JEIT210163

Citation:

WAN Honglin, WANG Xiaomin, PENG Zhenwei, BAI Zhiquan, YANG Xinghai, SUN Jiande. Dense Crowd Counting Algorithm Based on New Multi-scale Attention Mechanism[J]. Journal of Electronics & Information Technology, 2022, 44(3): 1129-1136. doi: 10.11999/JEIT210163

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Dense Crowd Counting Algorithm Based on New Multi-scale Attention Mechanism

doi: 10.11999/JEIT210163

1.
School of Physics and Electronic Science, Shandong Normal University , Jinan 250358, China
2.
School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
3.
School of Information Science and Engineering, Shandong University, Qingdao 266237, China
4.
School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, China

Funds: The National Natural Science Foundation of China (61971271), The Key Research and Development of Shandong Province (2018GGX106008)

Received Date: 2021-02-25
Accepted Date: 2021-11-05
Rev Recd Date: 2021-10-23

Available Online: 2021-11-11

Publish Date: 2022-03-28

Abstract

Abstract

Dense crowd counting is a classic problem in the field of computer vision, and it is still subject to the influence of factors such as uneven scale, noise and occlusion. This paper proposes a dense crowd counting method based on a new multi-scale attention mechanism. Deep network includes backbone network, feature extraction network and feature fusion network. Among them, the feature extraction network includes feature branch and attention branch. It adopts a new multi-scale module composed of parallel convolution kernel functions, which can better obtain the characteristics of people at different scales to adapt to the uneven scale of dense population distribution features; The feature fusion network uses the attention fusion module to enhance the output features of the feature extraction network, realizes the effective fusion of attention features and image features, and improves counting accuracy. Experiments on public data sets such as ShanghaiTech, UCF_CC_50, Mall and UCSD show that the proposed method outperforms existing methods in both MAE and MSE indicators.
- Crowd counting,
- New multi-scale attention,
- Convolutional neural network,
- Artificial intelligence

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References(33)

References

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