MSAGNet: crowd counting network based on multi-scale attention grading

Zhiwei Wu; Peirong Ji; Yan Chen; Mohammad S. Alam; Jun Sang

doi:10.1117/12.2663713

13 June 2023 MSAGNet: crowd counting network based on multi-scale attention grading

Zhiwei Wu, Peirong Ji, Yan Chen, Mohammad S. Alam, Jun Sang

Proceedings Volume 12527, Pattern Recognition and Tracking XXXIV; 125270S (2023) https://doi.org/10.1117/12.2663713
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Conference Poster

Abstract

In recent years, great progress has been made in the study of crowd counting. Although the crowd counting networks being proposed to solve different problems have achieved satisfactory counting results, the differences of crowd density and scale in the same scene still degrade the overall counting performance. In order to deal with this problem, we propose a Multi-Scale Attention Grading Crowd Counting Network (MSAGNet), which focuses on different crowd densities in the scene by attention mechanism and fuses multi-scale information to reduce scale differences. Specifically, the grading attention feature obtaining module focuses on different densities of people in the scene by attention mechanism, and adaptively assigns corresponding weights to different density regions. Dense regions are given more weights, allowing the model to focus more on that part making the training of that region more accurate and effective. In addition, the multi-scale density feature fusion module fuses the feature maps containing density information to generate the final feature maps. The obtained feature maps contain attention information at different scales, which are density mapped to obtain the estimated density maps. This method can focus on different density regions in the same scene, and simultaneously fuse multi-scale information and attention weight, which can effectively solve the problem of counting dense regions that is difficult to calculate. Extensive experiments on existing crowd counting datasets (UCF_CC_50, ShanghaiTech, UCF-QNRF) show that our method can effectively improve the counting performance.

Citation Download Citation

Zhiwei Wu, Peirong Ji, Yan Chen, Mohammad S. Alam, and Jun Sang "MSAGNet: crowd counting network based on multi-scale attention grading", Proc. SPIE 12527, Pattern Recognition and Tracking XXXIV, 125270S (13 June 2023); https://doi.org/10.1117/12.2663713

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available