  Crowd counting is gaining societal relevance, particularly in domains of
Urban Planning, Crowd Management, and Public Safety. This paper introduces
Fourier-guided attention (FGA), a novel attention mechanism for crowd count
estimation designed to address the inefficient full-scale global pattern
capture in existing works on convolution-based attention networks. FGA
efficiently captures multi-scale information, including full-scale global
patterns, by utilizing Fast-Fourier Transformations (FFT) along with spatial
attention for global features and convolutions with channel-wise attention for
semi-global and local features. The architecture of FGA involves a dual-path
approach: (1) a path for processing full-scale global features through FFT,
allowing for efficient extraction of information in the frequency domain, and
(2) a path for processing remaining feature maps for semi-global and local
features using traditional convolutions and channel-wise attention. This
dual-path architecture enables FGA to seamlessly integrate frequency and
spatial information, enhancing its ability to capture diverse crowd patterns.
We apply FGA in the last layers of two popular crowd-counting works, CSRNet and
CANNet, to evaluate the module's performance on benchmark datasets such as
ShanghaiTech-A, ShanghaiTech-B, UCF-CC-50, and JHU++ crowd. The experiments
demonstrate a notable improvement across all datasets based on
Mean-Squared-Error (MSE) and Mean-Absolute-Error (MAE) metrics, showing
comparable performance to recent state-of-the-art methods. Additionally, we
illustrate the interpretability using qualitative analysis, leveraging Grad-CAM
heatmaps, to show the effectiveness of FGA in capturing crowd patterns.
