SDANet: scale-deformation awareness network for crowd counting

Crowd counting aims to derive information about crowd density by quantifying the number of individuals in an image or video. It offers crucial insights applicable to various domains, e.g., secure, efficient decision-making, and management. However, scale variation and irregular shapes of heads pose...

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Bibliographic Details
Published in:Journal of electronic imaging 2024-07, Vol.33 (4), p.043002-043002
Main Authors: Wang, Jianyong, Guo, Xiangyu, Li, Qilei, Abdelmoniem, Ahmed M., Gao, Mingliang
Format: Article
Language:English
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Summary:Crowd counting aims to derive information about crowd density by quantifying the number of individuals in an image or video. It offers crucial insights applicable to various domains, e.g., secure, efficient decision-making, and management. However, scale variation and irregular shapes of heads pose intricate challenges. To address these challenges, we propose a scale-deformation awareness network (SDANet). Specifically, a scale awareness module is introduced to address the scale variation. It can capture long-distance dependencies and preserve precise spatial information by readjusting weights in height and width directions. Concurrently, a deformation awareness module is introduced to solve the challenge of head deformation. It adjusts the sampling position of the convolution kernel through deformable convolution and learning offset. Experimental results on four crowd-counting datasets prove the superiority of SDANet in accuracy, efficiency, and robustness.
ISSN:1017-9909
1560-229X
DOI:10.1117/1.JEI.33.4.043002