期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2000
卷号：XXXIII Part B5/1
页码：277-284
出版社：Copernicus Publications
摘要：Traffic flow survey for traffic control and planning is usually conducted with traffic beacons set up at limited roadside points. Therefore, they cannot observe the exact dynamic movement of vehicles which is important information for sophisticated traffic policy. On the other hand, stratospheric platform system has been recently projected in Japan. One of the purposes of the stratospheric platform system is utilization for earth observation. The stratospheric platform is expected to result in high spatial and time resolution images at specific areas for continuous observations. These high resolution and continuous images certainly make observation of vehicle movement easier. In this paper, we explored the possibility of vehicle tracking with high resolution and time-serial aerial images, which are on the assumption of the use of stratospheric platforms. In estimation of displacement vectors of vehicles , the most characteristic problem is that appearance/disappearance of vehicles occur, when they are under overhead bridges or shadows of buildings, or going outside the image, or so on. We employed the probabilistic relaxation method for tracking vehicles. And then we improved the probabilistic relaxation method by introducing (1) the color information of vehicles , and (2) the displacement vectors of each other. We applied the proposed method to simulated data and sample images, which were on a one-way street. The time interval of successive images was 1.5 seconds. The proposed method yielded a better result than the original method, and the rate of correct correspondence is above 95%. Furthermore, we also applied to the various time interval images. When time interval was less than 1.5 seconds, the result was good for vehicle tracking in this case
关键词：Object Tracking; Movement Detection; High Resolution Data/Images; Image Sequence; Platforms; ; Algorithms; Stochastic
