密集场景下的人群拥挤检测研究综述

doi:10.19678/j.issn.1000-3428.0069340

计算机工程 ›› 2026, Vol. 52 ›› Issue (3): 79-96. doi: 10.19678/j.issn.1000-3428.0069340

密集场景下的人群拥挤检测研究综述

许敏¹^,², 胡滨¹^,²^,³^,*()

1. 贵州大学计算机科学与技术学院公共大数据国家重点实验室, 贵州贵阳 550025
2. 贵州大学计算机科学与技术学院, 贵州贵阳 550025
3. 贵州大学人工智能研究院, 贵州贵阳 550025

收稿日期:2024-02-01 修回日期:2024-06-03 出版日期:2026-03-15 发布日期:2024-08-15
通讯作者: 胡滨
作者简介:
许敏, 女, 硕士研究生, 主研方向为计算智能、计算机视觉
胡滨(CCF高级会员、通信作者), 教授、博士、博士生导师
基金资助:
国家自然科学基金(62066006); 贵州省自然科学基金(黔科合基础[2020]1Y261); 贵州大学引进人才科研项目(贵大人基合字(2019)58号)

Survey of Research on Crowd Congestion Detection in Dense Scenarios

XU Min¹^,², HU Bin¹^,²^,³^,*()

1. State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, Guizhou, China
2. College of Computer Science and Technology, Guizhou University, Guiyang 550025, Guizhou, China
3. Artificial Intelligence Research Institute, Guizhou University, Guiyang 550025, Guizhou, China

Received:2024-02-01 Revised:2024-06-03 Online:2026-03-15 Published:2024-08-15
Contact: HU Bin

摘要/Abstract

摘要：

感知与检测公共场所密集人群发生的拥挤行为是计算机视觉领域极具挑战的课题。对该问题进行研究, 如人群运动特性分析、行为检测模型构建等, 可为揭示密集场景人群活动的运动特性和行为本质提供有益帮助, 同时可协助相关公共安全部门制定管理策略和应急响应措施, 从而有效避免人群灾难事件的发生与恶化。为此, 梳理与总结视觉场景下密集人群拥挤检测问题的研究工作。首先, 从人群动力学、社会力模型、流体力学理论等角度综述人群拥挤活动的定性特征; 其次, 调研现有人群拥挤检测算法及相关的计算模型; 接着, 给出此研究涉及的公共数据集与模型性能评估方法; 最后, 探讨人群拥挤检测研究的应用场景与未来研究方向。公共场所密集人群拥挤行为的定性、定量研究现状综述, 可为计算机视觉、智能监控、人工智能等领域的人群活动感知、行为分析理解、异常检测等提供有益参考。

关键词: 拥挤检测, 行为分析, 人群拥挤, 密集场景, 智能视频监控

Abstract:

Perceiving and detecting crowd congestion in public spaces is an extremely challenging task in computer vision. Research on this issue, such as analyzing the motion characteristics of crowds and constructing behavior detection models, can provide valuable insights into the motion traits and behavioral essence of crowd activities in dense scenarios. Additionally, it can assist relevant public safety departments in formulating management strategies and emergency response measures, thereby effectively preventing the occurrence and escalation of crowd-related disasters. To this end, this paper summarizes the research efforts on dense crowd congestion detection. First, an overview of the qualitative characteristics of crowd congestion from the perspectives of crowd dynamics, social force models, and fluid mechanics theory is presented. Second, existing crowd congestion detection algorithms and related computational models are investigated. Next, the public datasets and model performance evaluation methods relevant to this research are presented. Finally, the application scenarios and future research directions for crowd congestion detection are explored. A review of the current research status on the qualitative and quantitative analyses of dense crowd congestion behaviors in public spaces offers valuable references for crowd activity perception, behavior analysis and understanding, and anomaly detection in fields such as computer vision, intelligent surveillance, and artificial intelligence.

Key words: congestion detection, behavioral analysis, crowd congestion, dense scenarios, intelligent video surveillance

许敏, 胡滨. 密集场景下的人群拥挤检测研究综述[J]. 计算机工程, 2026, 52(3): 79-96.

XU Min, HU Bin. Survey of Research on Crowd Congestion Detection in Dense Scenarios[J]. Computer Engineering, 2026, 52(3): 79-96.

https://www.ecice06.com/CN/Y2026/V52/I3/79

图/表 18

图1 人群拥挤及灾难案例

Fig.1 Crowded crowds and disaster cases

图2 瓶颈疏散和拥挤分析

Fig.2 Analysis of bottleneck evacuation and crowding

图3 拥挤人群疏散测试

Fig.3 Crowd evacuation test

图4 人群奇异压力动量与密度的关系

Fig.4 The relationship between crowd singular pressure momentum and density

图5 人群拥挤检测技术的发展历程

Fig.5 The development of crowd congestion detection technology

图6 纹理特征提取的检测结果

Fig.6 Detection results of texture feature extraction

图7 高阶特征提取检测结果

Fig.7 Detection results of high-order feature extraction

图8 机器学习代表模型的拥塞检测结果

Fig.8 Congestion detection results of machine learning representative models

图9 人群拥挤数据集

Fig.9 Datasets of crowd congestion

图10 P-R曲线

Fig.10 P-R curves

图11 ROC曲线

Fig.11 ROC curves

图12 Khan评价方法操作流程

Fig.12 Operation process of Khan evaluation method

图13 智能视频监控应用

Fig.13 Application of intelligent video surveillance

图14 智慧景区游客流量分析平台

Fig.14 Smart scenic area tourist flow analysis platform

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