Crowd Counting in the Frequency Domain

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

doi:10.1109/CVPR52688.2022.01900

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 Proceedings › Conference paper (refereed) › Research › peer-review

53 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Publisher	IEEE
Pages	19586-19595
Number of pages	10
ISBN (Electronic)	9781665469463
ISBN (Print)	9781665469470
DOIs	https://doi.org/10.1109/CVPR52688.2022.01900
Publication status	Published - 2022
Externally published	Yes
Event	2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR - New Orleans, United States Duration: 19 Jun 2022 → 24 Jun 2022

Publication series

Name	Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume	2022-June
ISSN (Print)	1063-6919

Conference

Conference	2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR
Country/Territory	United States
City	New Orleans
Period	19/06/22 → 24/06/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Funding

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).

Keywords

Scene analysis and understanding
Vision applications and systems

Access to Document

10.1109/CVPR52688.2022.01900

Cite this

SHU, W., WAN, J., TAN, K. C., KWONG, S., & CHAN, A. B. (2022). Crowd Counting in the Frequency Domain. In Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 19586-19595). (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Vol. 2022-June). IEEE. https://doi.org/10.1109/CVPR52688.2022.01900

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title = "Crowd Counting in the Frequency Domain",

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.",

keywords = "Scene analysis and understanding, Vision applications and systems",

author = "Weibo SHU and Jia WAN and TAN, {Kay Chen} and Sam KWONG and CHAN, {Antoni B.}",

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year = "2022",

doi = "10.1109/CVPR52688.2022.01900",

language = "English",

isbn = "9781665469470",

series = "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition",

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SHU, W, WAN, J, TAN, KC, KWONG, S & CHAN, AB 2022, Crowd Counting in the Frequency Domain. in Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2022-June, IEEE, pp. 19586-19595, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR , New Orleans, United States, 19/06/22. https://doi.org/10.1109/CVPR52688.2022.01900

Crowd Counting in the Frequency Domain. / SHU, Weibo; WAN, Jia; TAN, Kay Chen et al.
Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 2022. p. 19586-19595 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Vol. 2022-June).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

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AU - WAN, Jia

AU - TAN, Kay Chen

AU - KWONG, Sam

AU - CHAN, Antoni B.

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

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Crowd Counting in the Frequency Domain

Abstract

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Keywords

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