Statistical Estimation from Videos of Freeway Traffic

这项拟议的研究集中于从高速公路交通的视频记录中估计速度、密度和流场，使用来自伯克利公路实验室的数据，该实验室已经记录了I-80高速公路上数千小时的交通流量。调查人员特别关注没有很好解决的记录，以便明确地识别和跟踪车辆。他们建议调查两大类方法。首先，建立在最初令人鼓舞的方法基础上，建立在速度场的局部半参数统计模型的基础上。第二种是基于使用微观模型的模拟。这些都是对统计方法和理论发展的挑战。更广泛的影响包括：开发有效的、实用的交通视频监控方法，这将是广泛实用的；编制大量的速度场经验实体，这将是交通流理论研究的宝贵资源；将方法扩展到在计算机视觉中广泛应用的更一般的二维光流估计问题；以及本科生和研究生参与研究。

The proposed research focuses on the estimation of velocity, density, andflow fields from video recordings of freeway traffic, using datafrom the Berkeley Highway laboratory, which has recorded thousandsof hours of traffic flow on freeway I-80. The investigators are particularlyconcerned with recordings which are not sufficiently well resolvedto allow unambiguous vehicle identification and tracking.They propose to investigate two broad classes of methods. Thefirst, building on an initially encouraging approach, is basedupon local semiparametric statistical modelling of the velocityfield. The second is based upon simulation using microscopicmodels. Both of these present challenges for the development ofstatistical methodology and theory.Broader impacts include: the development of effective, practicalmethods for video surveillance of traffic, which would be ofwidespread utility; the compilation of a large empirical body ofvelocity fields, which will be a valuable resource for researchersin traffic flow theory; extension of the methodology to the moregeneral problem of estimating optical flow in two dimensions,which has widespread applications in computer vision; and theinvolvement of undergraduates and graduate students in research.