CAREER: Passive Vision -- What Can Be Learned by a Stationary Observer

职业：被动视觉——固定观察者可以学到什么

• 批准号: 0546383
• 负责人: Robert Pless
• 金额: $ 50万
• 依托单位: Washington University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-03-01 至 2012-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0546383&HistoricalAwards=false
• 关键词: CAREER; Passive; Vision; What; Learned

CAREER: Passive Vision -- What Can Be Learned by a Stationary ObserverPassive Vision is the analysis of video from a camera that is not moving. Many cameras do not move, and continually watch a specific scene -- an ATM, an airport security desk, or a traffic intersection -- for months or years. Much as Active Vision (the ability to intentionally control camera motion) simplifies problems in structure from motion, Passive Vision simplifies statistical image analysis by observing statistics of the same scene for very long time periods.This project develops a framework to study the statistics of fixed-viewpoint video. General statistics of natural video underlie current models of image and video compression and provide a statistical context for general image processing. But for video taken from a single viewpoint, the same analytic tools find much more specific statistical correlations, and these correlations relate to important scene features. For example, image regions that share geometric features such as surface normal and depth have a correlated responses to natural lighting changes. A tree waving in the wind tends to move all at the same time.Furthermore, automated tools that develop statistics of specific video sequences, accumulated over time, promise to ground a number of probabilistic algorithms in surveillance. Surprisingly simple, local statistics of image derivatives find anomalous objects in scenes with significant background motions and find complicated patterns of motions of objects in a scene. Within surveillance, characterizing the statistics of background variations captured over weeks or months provides a foundation to more formally address questions of slow background drift (due to clouds, shadows, or seasons), and when or whether moving objects that stop should be included in the background.This research program formalizes heuristic approaches to key problems in surveillance and offers a broader understanding of the statistics of natural images. This provides the foundation for a potentially large body of research in learning scene-specific algorithms for image representation and coding, image de-noising, object recognition, anomaly detection, and scene annotation --- key problems in using Computer Vision to address current Homeland Security needs.Project web page:http://www.cse.wustl.edu/~pless/PassiveVision.html

职业：被动视觉--静止的观察者能学到什么被动视觉是对来自不动的摄像机的视频进行分析。 许多摄像头不会移动，而是连续几个月或几年地监视特定的场景-自动取款机，机场安检台或交通路口。 就像主动视觉（主动控制摄像机运动的能力）简化了从运动到结构的问题一样，被动视觉通过观察同一场景的统计数据来简化统计图像分析。本项目开发了一个框架来研究固定视点视频的统计数据。 自然视频的一般统计数据是当前图像和视频压缩模型的基础，并为一般图像处理提供统计背景。 但是对于从单一视角拍摄的视频，相同的分析工具发现了更具体的统计相关性，并且这些相关性与重要的场景特征有关。 例如，共享诸如表面法线和深度的几何特征的图像区域具有对自然光照变化的相关响应。 一棵在风中摇曳的树往往会同时移动。此外，自动化工具可以对特定的视频序列进行统计，随着时间的推移而积累，有望为监控中的许多概率算法奠定基础。令人惊讶的简单，图像导数的局部统计发现具有显著背景运动的场景中的异常对象，并且发现场景中的对象的运动的复杂模式。 在监测中，表征在数周或数月内捕获的背景变化的统计数据为更正式地解决缓慢背景漂移问题提供了基础（由于云、阴影或季节），以及何时或是否应将停止的移动物体包括在背景中。该研究计划正式提出了启发式方法来解决监视中的关键问题，并提供了对自然图像统计的更广泛理解。 这为潜在的大量研究提供了基础，这些研究涉及学习特定场景的图像表示和编码算法、图像去噪、对象识别、异常检测和场景注释--这些是使用计算机视觉解决当前国土安全需求的关键问题。http://www.cse.wustl.edu/~pless/PassiveVision.html