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

Washington University, Saint Louis MO

Investigators

Abstract

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