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Alternative TitleResearch on technology of Intelligent Crowd Surveillance
Thesis Advisor刘昌平 ; 黄磊
Degree Grantor中国科学院研究生院
Place of Conferral中国科学院自动化研究所
Degree Discipline模式识别与智能系统
Keyword人群密度估计 人群流量统计 智能监控 Crowd Density Estimation Crowd Volume Counting Intelligent Surveillance
Abstract随着社会经济的发展,各种大型公共场所中的人群聚集现象愈加频繁。 智能化的人群监控与管理,具有深远的研究意义和迫切的实际需求。 本文工作旨在利用计算机视觉和模式识别的相关理论知识,获取监控视频中人群的密度分布和流量信息,为智能化的人群管理提供技术保障。 本文研究针对实际应用中的普通视频监视设备,要求分析算法的时间复杂度较低,可实时运行。 本文首先介绍了基于实际监控视频构建的人群监控数据库CASIA-Crowd,包括人群密度数据库和人群流量数据库。 本文统一了人群密度划分标准和标注方案,并进行了行人个体和流量数据的多层次标注。 针对人群密度估计,本文提出基于图像块纹理分析的方法, 通过划分图像块,提取纹理特征并进行统计学习分类,获取局部密度信息,进而综合得到整体密度信息。 该方法具有较好的泛化性能和抗背景干扰的能力。 本文提出了两种新的纹理特征:ALBP特征和GOCM特征,并提出了基于bag of words模型的人群纹理基元组成描述方法,深入刻画了人群纹理的本质属性。 针对人群密度等级的“渐变性”和“相邻相似性”特点,本文提出一种基于置信度分析和统计学习的人群密度多类别分类方法。 该方法通过基于二叉树的纠错输出编码以及基于信道传输模型的解码算法,有效组合多个二分类器, 不仅提高了人群密度分类正确率而且增强了分类器的泛化性能。 针对人群流量统计,本文采用了基于行人检测跟踪的方法。 在行人检测环节,首先利用运动特征和角点特征,预估行人区域;然后利用行人外观模板进行滑窗搜索,精确定位目标。 这种级联式的搜索策略在提高检测效率的同时降低了误检率。在行人跟踪环节,本文提出特征点引导的行人跟踪方法。 借助特征点与行人的同步运动关系,通过特征点跟踪和概率推理得到目标位置。 该方法可用于中高密度流动人群的个体跟踪,获取个体运动轨迹,再辅以绊线检测算法,即可实现指定方向的人群流量统计。 本文的人群密度估计和人群流量统计算法现已成功应用于国家奥林匹克公园景区智能人群监控系统,为景区人群管理实现信息化和智能化做出了贡献!
Other AbstractAlong with the rapid development of socio-economic, crowd and congestion in large-scale public places are rising. It is timely and meaningful to implement intelligent crowd surveillance and crowd management. The work aims at analyzing the density distribution and volume statistics of crowd in surveillance videos, which are crucial for intelligent crowd management. In view of the limited performance of normal surveillance apparatus in actual applications, the analysis algorithms should have low time complexity, to enable real-time processing. A database of crowd surveillance named as "CASIA-Crowd" is described firstly in this paper. It is divided into two parts: the crowd density database and the crowd volume database, both of which are constructed from actual surveillance videos. An uniform measurement of crowd density is proposed, together with the corresponding labeling method. Multilayered labeling is executed on pedestrian individual and volume data. As for crowd density estimation, a novel method based on the texture analysis of "image patch" is proposed. A set of image patches are generated from the video frame, and then local density level is determined by texture analysis and statistical classification. Finally, information of local density is synthesized, formulating the final result of total density. The method does well in generalization and noise-anticipation. Two novel texture features are proposed: ALBP and GOCM, as well as a texton composition description based on the bag of words model. All of the above are designed to depict the intrinsic attributes of crowd texture. Considering that crowd density is "gradient" between neighboring levels, a multi-category classification method that based on confidence analysis and statistical learning is proposed. The method combines several binary classifiers together, by binary tree based ECOC encoding and channel transmission based decoding. The method not only increases accuracy but also improves the generalization performance. Crowd volume counting is accomplished by pedestrian detection and tracking. During the detection stage, firstly, the information of motion and corner is utilized to make a preliminary estimate of pedestrian's location; and then the sliding window search based on the appearance template of human is performed, to locate objects precisely. The two-stage searching strategy not only improves efficiency of detection, but also reduces false alarms. During the tracking stage, fe...
Other Identifier200718014628057
Document Type学位论文
Recommended Citation
GB/T 7714
麻文华. 智能人群监控中的关键技术研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2010.
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