Passive Vision

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, clouds over a city, 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.

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 lighting changes. A tree waving in the wind tends to move all at the same time. The statistics of natural image variations that are not due to camera motion have not been well characterized, limiting the general understanding of the statistics of natural video.

Sub-projects

• Improving surveillance by computing long-term statistics of background and object appearance, including both multi-temporal background models (project page), and location specific shape priors (project page).
• StatiC: Understanding images from a large collection of outdoor cameras scattered (mostly) across the United States, and partially integrated with Google Maps (project page).