基于区域上下文感知的人群计数方法

doi:10.16451/j.cnki.issn1003-6059.202512004

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Abstract To address the challenges of uneven density distribution and large scale variations in complex crowd scenes, existing Transformer-based methods typically overlook the utilization of spatial and channel information while handling cross-scale contextual features. Therefore, a method for crowd counting based on regional context awareness(RCA) is proposed. First, a region guidance module is designed to adaptively assign an attention region for each feature location. Thereby region-level context is introduced and non-uniform density distributions are better accommodated. Second, a spatial-channel context awareness module is designed to enable feature interaction across spatial and channel dimensions. Consequently, cross-dimensional regional dependencies are constructed and the discrimination between foreground and background regions is enhanced. Finally, a distribution-level constraint is introduced during the training to improve the consistency between the predicted density distribution and the ground-truth distribution. Experimental results on JHU-Crowd++, ShanghaiTech A, and ShanghaiTech B datasets validate the robustness and generalization capability of RCA in complex scenes.

Key words： Crowd Counting Region Guidance Attention Mechanism Context Awareness

Received: 25 November 2025

ZTFLH:

TP391

Fund:General Project of National Natural Science Foundation of China(No.62272141), Fundamental Research Funds for the Central Universities(No.JZ2025HGTG0291)

Corresponding Authors: GAO Xinjian, Ph.D., associate professor. His research interests include image processing, deep learning, artificial intelligence, and machine learning.

About author:: HONG Zhiyuan, Master student. His research interests include crowd counting, small object detection, and computer vision.
Ren Mengyan, Master student. Her research interests include small object detection, infrared small target detection, and computer vision.
Wang Xilin, Master student. His research interests include object detection and compu-ter vision.
GAO Jun, Ph.D., professor. His research interests include image processing, pattern recognition, and intelligent information processing.

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	HONG Zhiyuan
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	GAO Jun

Cite this article:

HONG Zhiyuan,GAO Xinjian,REN Mengyan等. Crowd Counting Based on Regional Context Awareness[J]. Pattern Recognition and Artificial Intelligence, 2025, 38(12): 1108-1120.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202512004 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2025/V38/I12/1108

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