A New Technique for Measurement of Traffic Flow on the Proposed High Altitude Platforms

一种在高空平台上测量交通流量的新技术

基本信息

批准号：
13450208
负责人：
SHIMIZU Eihan
金额：
$ 1.79万
依托单位：
The University of Tokyo
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
2001
资助国家：
日本
起止时间：
2001 至 2003
项目状态：
已结题

项目摘要

Sequential images from high altitude platforms are suitable for traffic flow surveillance, which requires fixed-point continuous observation of vehicle movements over a wide area. High altitude platforms are expected to result in high spatial and time resolution images at specific areas for continuous observation. These high resolution and continuous images certainly make observation of vehicle movement easier. As a result, the high altitude platforms have a great potential with wider scope of utilization to derive useful traffic information on vehicle trajectories. In this study, we explore the possibility of vehicle maneuvers recognition with high resolution and time-serial aerial images, which are on the assumption of the use of helicopters and stratospheric platforms.Specifically, we develop a new technique for vehicle maneuvers recognition. In accordance with human perception, we develop spatio-temporal clustering method, which is composed of geometric correction, background subtraction, shadow detection, optical flow extraction, clustering and labeling. Employing the two features, that are background subtraction value and optical flow, the pixels that are adjacent and have similar features are grouped. Consequently, vehicle clusters are formed in the spatio-temporal image.The method, however, required adjustments to some parameters, depending on the images. In addition, the results were given in image coordinates (pixels) instead of real world coordinates. This research describes a method to adjust the parameters and a framework to evaluate the vehicle positioning accuracy using real distances.The proposed framework is applied to real sequential images obtained from a helicopter, and the vehicle positioning accuracy is estimated. The sequential images have 10cm, 30 cm spatial resolution and 1/30 s time resolution. The framework provides exact and efficient vehicle recognition. The positioning accuracy of the recognized vehicles is better than 1.92m.

高空平台的连续图像适用于交通流量监控，需要对大范围内的车辆运动进行定点连续观察。高空平台预计将在特定区域产生高空间和时间分辨率的图像，以供连续观测。这些高分辨率和连续的图像无疑使观察车辆运动变得更加容易。因此，高空平台具有巨大的潜力和更广泛的用途，可以获取有关车辆轨迹的有用交通信息。在本研究中，我们探讨了在使用直升机和平流层平台的假设下，利用高分辨率和时间序列航空图像进行车辆机动识别的可能性。具体来说，我们开发了一种车辆机动识别的新技术。根据人类感知，我们开发了时空聚类方法，该方法由几何校正、背景扣除、阴影检测、光流提取、聚类和标记组成。利用背景扣除值和光流这两个特征，将相邻且具有相似特征的像素分组。因此，在时空图像中形成车辆集群。然而，该方法需要根据图像调整一些参数。此外，结果以图像坐标（像素）而不是现实世界坐标给出。本研究描述了一种调整参数的方法和一个使用真实距离评估车辆定位精度的框架。所提出的框架应用于从直升机获得的真实序列图像，并估计车辆定位精度。连续图像具有10cm、30cm空间分辨率和1/30s时间分辨率。该框架提供准确、高效的车辆识别。识别车辆定位精度优于1.92m。