Statistical Estimation from Videos of Freeway Traffic

拟议的研究重点是估计速度，密度和流场的视频记录的高速公路交通，使用的数据从伯克利高速公路实验室，它记录了数千小时的交通流量在高速公路I-80。 研究人员特别关注的是那些分辨率不够好，无法进行明确的车辆识别和跟踪的记录。 第一，建立在一个最初令人鼓舞的方法，是basedupon当地的半参数统计模型的velocityfield。 第二种是基于微观模型的模拟。 这两个问题都对统计方法和理论的发展提出了挑战，更广泛的影响包括：发展有效的、实用的交通视频监控方法，这将具有广泛的实用性;汇编大量的速度场经验数据，这将是交通流理论研究者的宝贵资源;扩展的方法，以更一般的问题，估计光流在两个层面，这在计算机视觉中有广泛的应用;和本科生和研究生的参与研究。

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.