Crowd Counting Method Based on Multi-Scale Enhanced Network

Tao XU; Yinong DUAN; Jiahao DU; Caihua LIU

doi:10.11999/JEIT200331

Volume 43 Issue 6

Jun. 2021

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Tao XU, Yinong DUAN, Jiahao DU, Caihua LIU. Crowd Counting Method Based on Multi-Scale Enhanced Network[J]. Journal of Electronics & Information Technology, 2021, 43(6): 1764-1771. doi: 10.11999/JEIT200331

Citation:

Tao XU, Yinong DUAN, Jiahao DU, Caihua LIU. Crowd Counting Method Based on Multi-Scale Enhanced Network[J]. Journal of Electronics & Information Technology, 2021, 43(6): 1764-1771. doi: 10.11999/JEIT200331

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Crowd Counting Method Based on Multi-Scale Enhanced Network

doi: 10.11999/JEIT200331

1.
College of Computer Science and Technology, Civil Aviation University of China, Tianjin 300300, China
2.
Information Technology Base of Civil Aviation Administration of China, Civil Aviation University of China, Tianjin 300300, China

Funds: The Natural Science Foundation of Tianjin (18JCYBJC85100), The Fundamental Research Funds for the Central Universities from the Civil Aviation University of China (3122018C024), The Scientific Research Startup Project of the Civil Aviation University of China (2017QD16X)

Received Date: 2020-04-28
Rev Recd Date: 2020-10-12

Available Online: 2020-10-16

Publish Date: 2021-06-18

Abstract

Abstract

The performance of the crowd counting methods is degraded due to the commonly used Euclidean loss ignoring the local correlation of images and the limited ability of the model to cope with multi-scale information. A crowd counting method based on Multi-Scale Enhanced Network(MSEN) is proposed to address the above problems. Firstly, an embedded GAN module with a multi-branch generator and a regional discriminator is designed to initially generate crowd density maps and optimize their local correlation. Then, a well-designed scale enhancement module is connected after the embedded GAN module to extract further local features of different scales from different regions, which will strengthen the generalization ability of the model. Extensive experimental results on three challenging public datasets demonstrate that the performance of the proposed method can effectively improve the accuracy and robustness of the prediction.
- Crowd counting,
- Image local correlation,
- Multi-scale feature,
- Embedded GAN module,
- Scale-enhancement module

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References(20)

References

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