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

职业：被动视觉——静止观察者能学到的东西被动视觉是对不移动的摄像机拍摄的视频进行分析。许多摄像机不会移动，而是连续数月或数年地监视一个特定的场景——自动取款机、机场安检台或十字路口。就像主动视觉（有意控制摄像机运动的能力）从运动中简化了结构问题一样，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。本项目开发了一个研究固定视点视频统计的框架。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背景。但对于从单一视点拍摄的视频，同样的分析工具发现了更具体的统计相关性，这些相关性与重要的场景特征有关。例如，共享几何特征的图像区域，如表面法线和深度，对自然光的变化有相关的响应。在风中摇摆的树往往会同时移动。此外，随着时间的推移，开发特定视频序列统计数据的自动化工具有望为监控中的许多概率算法奠定基础。令人惊讶的是，图像导数的局部统计可以在具有明显背景运动的场景中发现异常物体，并发现场景中物体运动的复杂模式。在监视中，描述在几周或几个月内捕获的背景变化的统计特征为更正式地解决缓慢背景漂移（由于云、阴影或季节）的问题以及何时或是否应该将移动物体包含在背景中提供了基础。该研究计划形式化了对监视中的关键问题的启发式方法，并提供了对自然图像统计的更广泛理解。这为学习图像表示和编码、图像去噪、对象识别、异常检测和场景注释等场景特定算法的潜在大量研究提供了基础——这些都是使用计算机视觉解决当前国土安全需求的关键问题。项目网页：http://www.cse.wustl.edu/~pless/PassiveVision.html

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