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

职业：被动视觉--静止的观察者可以学到的东西被动视觉是对静止不动的摄像机拍摄的视频进行分析。许多摄像头一动不动，连续观察一个特定的场景--自动取款机、机场安检台或交通十字路口--几个月或几年。就像主动视觉(有意控制摄像机运动的能力)简化了运动中的结构问题一样，被动视觉通过长时间观察同一场景的统计数据来简化统计图像分析。这个项目开发了一个框架来研究固定视点视频的统计。自然视频的一般统计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上下文。但对于从单一视点拍摄的视频，相同的分析工具会发现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，共享曲面法线和深度等几何特征的图像区域具有对自然光照变化的相关响应。一棵在风中摇摆的树往往会同时移动。此外，自动化工具可以对特定视频序列进行统计，随着时间的推移积累起来，有望在监控中使用一些概率算法。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，并发现场景中对象的复杂运动模式。在监控中，表征在几周或几个月内捕获的背景变化的统计数据，为更正式地解决缓慢的背景漂移(由于云、阴影或季节)以及何时或是否应该将停止的移动对象包括在背景中的问题提供了基础。该研究计划将启发式方法正式化，以解决监控中的关键问题，并提供对自然图像统计的更广泛的理解。这为学习图像表示和编码、图像去噪、目标识别、异常检测和场景注释的特定场景算法的潜在大量研究提供了基础-使用计算机视觉来解决当前国土安全需求的关键问题。Web page:http://www.cse.wustl.edu/~pless/PassiveVision.html项目

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