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Updated: Sep 20, 2025

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

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Published on: January 18, 2020

Spatial-Frequency Attention Network for Crowd Counting.

Xiangyu Guo¹, Mingliang Gao¹, Wenzhe Zhai¹

¹School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo, China.

|June 9, 2022

Summary

This summary is machine-generated.

This study introduces a novel spatial-frequency attention network (SFANet) to accurately count people in crowded scenes. The SFANet improves crowd counting accuracy and robustness by adaptively focusing on relevant spatial and frequency features.

Keywords:

convolutional neural network crowd counting density estimation spatial-frequency attention

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Published on: October 24, 2012

Area of Science:

Computer Vision
Deep Learning
Artificial Intelligence

Background:

Crowd counting is vital for urban security and video surveillance.
Existing deep learning models struggle with large-scale variations in dense crowds.

Purpose of the Study:

To propose a novel Spatial-Frequency Attention Network (SFANet) for improved crowd counting.
To enhance the accuracy and robustness of crowd counting in complex scenarios.

Main Methods:

Developed a bottleneck spatial attention module to emphasize features in various spatial locations.
Integrated a multispectral channel attention module to capture comprehensive frequency components.
Combined spatial and frequency attention mechanisms for mutual promotion and focused feature extraction.

Main Results:

SFANet achieved state-of-the-art performance on five benchmark crowd datasets.
Demonstrated superior accuracy and robustness in crowd counting tasks.
Effectively suppressed misleading information by focusing on discriminative regions.

Conclusions:

The proposed SFANet effectively addresses limitations in current crowd counting methods.
The combined spatial-frequency attention approach offers significant improvements in accuracy and robustness.
SFANet shows great potential for real-world applications in video surveillance and urban security.