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.

来自高空平台的顺序图像适合交通流量监视，这需要在宽面积上连续观察车辆移动。高空平台有望在特定区域可导致高空间和时间分辨率图像进行连续观察。这些高分辨率和连续的图像无疑使对车辆运动的观察更加容易。结果，高空平台具有较大利用范围，以获取有关车辆轨迹的有用流量信息的范围。在这项研究中，我们探讨了使用高分辨率和时间串行的航空图像识别车辆操纵的可能性，这是在使用直升机和平流层平台的假设上。根据人类的感知，我们开发了时空聚类方法，该方法由几何校正，背景减法，阴影检测，光流萃取，聚类和标记组成。采用这两个功能，即背景减法值和光流，将相邻且具有相似特征的像素分组。因此，在时空图像中形成了车辆簇。但是，该方法需要对某些参数进行调整，具体取决于图像。此外，结果是在图像坐标（像素）而不是现实世界坐标中给出的。这项研究描述了一种调整参数和框架的方法，以使用实际距离来评估车辆定位精度。提出的框架应用于从直升机获得的真实顺序图像，并估计了车辆定位精度。顺序图像具有10厘米，30 cm的空间分辨率和1/30 s的时间分辨率。该框架提供了精确有效的车辆识别。公认的车辆的定位精度优于1.92m。