Crowd Counting in the Frequency Domain

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

36 Scopus Citations
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Detail(s)

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022
PublisherInstitute of Electrical and Electronics Engineers, Inc.
Pages19586-19595
ISBN (electronic)978-1-6654-6946-3
ISBN (print)978-1-6654-6947-0
Publication statusPublished - 2022

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919
ISSN (electronic)2575-7075

Conference

Title2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022)
LocationHybrid
PlaceUnited States
CityNew Orleans
Period19 - 24 June 2022

Abstract

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. Our codes will be available at: wbshu/Crowd Counting in the Frequency Domain

Research Area(s)

  • Scene analysis and understanding, Vision applications and systems

Citation Format(s)

Crowd Counting in the Frequency Domain. / Shu, Weibo; Wan, Jia; Tan, Kay Chen et al.
Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022. Institute of Electrical and Electronics Engineers, Inc., 2022. p. 19586-19595 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review