Crowd Counting in the Frequency Domain

Weibo SHU, Jia WAN, Kay Chen TAN, Sam KWONG, Antoni B. CHAN

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

32 Citations (Scopus)


This paper investigates crowd counting in the frequency domain, which is a novel direction compared to the traditional view in the spatial domain. By transforming the density map into the frequency domain and using the properties of the characteristic function, we propose a novel method that is simple, effective, and efficient. The solid theoretical analysis ends up as an implementation-friendly loss function, which requires only standard tensor operations in the training process. We prove that our loss function is an upper bound of the pseudo sup norm metric between the ground truth and the prediction density map (over all of their sub-regions), and demonstrate its efficacy and efficiency versus other loss functions. The experimental results also show its competitiveness to the state-of-the-art on five benchmark data sets: ShanghaiTech A & B, UCF-QNRF, JHU++, and NWPU.

Original languageEnglish
Title of host publicationProceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Number of pages10
ISBN (Electronic)9781665469463
ISBN (Print)9781665469470
Publication statusPublished - 2022
Externally publishedYes
Event2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR - New Orleans, United States
Duration: 19 Jun 202224 Jun 2022

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919


Conference2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR
Country/TerritoryUnited States
CityNew Orleans

Bibliographical note

Funding Information:
Acknowledgements. This work was supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Proj. No. CityU 11212518), and a Strategic Research Grant from City University of Hong Kong (Proj. No. 7005665).

Publisher Copyright:
© 2022 IEEE.


  • Scene analysis and understanding
  • Vision applications and systems


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