被动视觉:全球网络摄像头成像网络

Nathan Jacobs, Richard Souvenir, Robert Pless
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引用次数: 3

摘要

网络上有大量的实时摄像头,可以拍摄公园、道路、城市、海滩、山脉、建筑物和停车场。有各种各样的问题可以有效地利用这种大规模分布的、可扩展的、已经存在的摄像机网络。为了实现这一目标,本文讨论了AMOS(许多户外场景档案)数据库正在进行的研究,该数据库包括过去3年中每半小时拍摄的1000台摄像机的图像。特别是,我们提供(1)仅从图像数据进行地理定位和校准这些相机的算法,(2)一套工具来注释视图中的场景部分(例如地平面,道路,天空,树木),以及(3)仅从图像数据自动推断天气信息(例如风速,蒸汽压)的算法进展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Passive vision: The global webcam imaging network
The web has an enormous collection of live cameras that image parks, roads, cities, beaches, mountains, buildings, parking lots. There are a wide variety of problems that could effectively use this massively distributed, scalable, and already existing camera network. To move towards this goal, this paper discusses ongoing research with the AMOS (Archive of Many Outdoor Scenes) database, which includes images from 1000 cameras captured every half hour over the last 3 years. In particular, we offer (1) algorithms for geo-locating and calibrating these cameras just from image data, (2) a set of tools to annotate parts of the scene in view (e.g. ground plane, roads, sky, trees), and (3) advances in algorithms to automatically infer weather information (e.g. wind-speed, vapor pressure) from image data alone.
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