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基于光流方法的人数统计与人群密度估计
Alternative TitlePeople counting and crowd density estimation using optical flow
高从文
Subtype工学硕士
Thesis Advisor黄凯奇
2011-05-25
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人数统计与人群密度估计 光流 宏观特征 前景提取 People Counting And Crowd Density Estimation Optical Flow Macroscopic Feature Foreground Segmentation
Abstract随着世界范围的人口增长和城镇化进程,人群控制和公共空间设计变得越发重要。在众多用于这方面应用的群信息中,人数值和人群密度是一项重要的信息,因为不同的人群密度通常需要给予不同的关注度。此外人数统计值是大型商场、购物中心、连锁店、车站、展览馆、体育场等公众易于聚集场所在管理和决策方面不可缺少的数据。对零售业而言,人流量更是非常基础的指标。作为一个以应用为导向的研究方向,人数统计和人群密度估计方法的研究也随着应用的需求不断发展,从关注密度值到关注人数值,从简单的线性回归到非线性回归。 本文的工作基于视频的运动信息,而光流则用于捕获这种运动信息。主要的工作和贡献有: 1. 提出一种基于混合特征的人数统计方法。过去的人数统计与人群密度估计方法都是利用一些基于像素点或者区域的特征,根据整体论的观点,系统的特性不但由其各个组成部分表现出来,也通过其整体表现出来。对于人群来说,其特性不但由每个个体或者微观的特征来描述,也可以由整个人群的整体特性来描述。基于此,提出了一种新的特征——宏观特征,它是通过利用光流获取整个人群的运动信息得到的。因为不同拥挤程度的人群所表现出来的运动特性是不同的,对于较为稀疏的人群,行人可以随意走动,但是对于拥挤的人群,行人只能做一些微小的运动。相对于宏观特征,我们称此前的基于像素点或者部分区域的特征为微观特征。最后将这两部分特征结合起来组成混合特征,作为人数统计的底层特征。 2. 对基于马赛克图像差分的前景提取方法进行改进。在基于前景提取的人数统计与人群密度估计方法中,前景提取是十分关键的步骤。在人群较为拥挤的情况下,每个人的运动都很细微,这就使得一些较为常用的基于统计的前景提取方法(例如混合高斯模型)容易将这些细微的运动误当做背景处理,为解决这一问题,提出了基于马赛克图像差分的前景提取方法。但是这种方法受光照变化以及阴影的影响较大,并且对于同一场景中有多群不同运动速度人群的情况难于调节参数。为消除上述局限性,提出了基于光流的区分方法。由于阴影与行人具有不同的运动形式,不同速度的人群的运动也存在差别,可以对场景区域进行划分,找出阴影区域,对于不同的运动人群区域赋予不同的参数组合,从而达到去除阴影,提高方法参数自适应性的目的。 总的来说,本文利用人群的运动信息提出了宏观特征,并且将其与微观特征融合应用于人数统计,取得不错的效果。另一方面又对一种用于人数统计与人群密度估计的前景提取方法——基于马赛克图像差分的前景提取方法进行了有效地改进。
Other AbstractCrowd management and public space design become more and more important with population growth and world-wideurbanization. Crowd count or density is an important feature among the crowd information applied in this area, forcrowds of different density should receive a different level of attention. Accurate customer counting has become a key component as a holistic performance measurement for supermarkets, shopping malls, chain stores, museums, sporting venues, airports and so on. People counting and density estimation is a research direction taking practical application as the guidance, which makes this direction develop along with market demand. The key of research has developed from density estimation to people counting, the regression method from linearity to nonlinearity. The new methods proposed in this thesis are both based on motion information in a video and then optical flow is adopted to capture the information. The main contributions of this thesis include: 1. People counting using combined feature. The features adopted in previous methods are all extracted at pixel-level or based on local area, which are severely affected by factors such as occlusion. According to Holism, the properties of a given system are not only explained by its component parts but also the system as a whole. For people counting, we can model a crowd to extract macroscopic feature. The pedestrians perform different behaviors in crowds of different degree of crowdedness: when the crowd density is low, pedestrians are free to walk about, while the crowd density is high, they are hardly able to lift each foot. We can model the crowd with respect to these changes in behavior and apply optical flow to capture this motion information. Here we call the features used in previous work microscopic feature. Afterwards, we concatenate microscopic and macroscopic features into a combined feature vector and map it to people count using neural network. 2. Improvements on Mosaic Image Difference based foreground segmentation using optical flow. Foreground segmentation stage is a critical component for crowd density estimation models based on foreground segmentation. In places with people gathering and waiting, statistical background models can not segment accurate foreground because they progressively update as time goes by, this progress integrates stationary crowd (foreground elements) into background model simultaneously. To settle this problem, the Mosaic Image Difference (MID) ...
shelfnumXWLW1647
Other Identifier200828014628092
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/7591
Collection毕业生_硕士学位论文
Recommended Citation
GB/T 7714
高从文. 基于光流方法的人数统计与人群密度估计[D]. 中国科学院自动化研究所. 中国科学院研究生院,2011.
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