Automating tools for geo-locating and geo-orienting static cameras is a key step in creating a useful global imaging network from cameras attached to the Internet. We present algorithms for partial camera calibration that rely on access to accurately time-stamped images captured over time from cameras that do not move. To support these algorithms we also offer a method of camera viewpoint change detection, or "tamper detection", which determines if a camera has moved in the challenging case when images are only captured every half hour. These algorithms are tested on a subset of the AMOS (Archive of Many Outdoor Scenes) database, and we present preliminary results that highlight the promise of these approaches.

This paper details an empirical study of large image sets taken by static cameras. These images have consistent correlations over the entire image and over time scales of days to months. Simple second-order statistics of such image sets show vastly more structure than exists in generic natural images or video from moving cameras. Using a slight variant to PCA, we can decompose all cameras into comparable components and annotate images with respect to surface orientation, weather, and seasonal change. Experiments are based on a data set from 538 cameras across the United States which have collected more than 17 million images over the the last 6 months.

A key problem in widely distributed camera networks is locating the cameras. This paper considers three scenarios for camera localization: localizing a camera in an unknown environment, adding a new camera in a region with many other cameras, and localizing a camera by finding correlations with satellite imagery. We find that simple summary statistics (the time course of principal component coefficients) are sufficient to geolocate cameras without determining correspondences between cameras or explicitly reasoning about weather in the scene. We present results from a database of images from 538 cameras collected over the course of a year. We find that for cameras that remain stationary and for which we have accurate image times- tamps, we can localize most cameras to within 50 miles of the known location. In addition, we demonstrate the use of a distributed camera network in the construction a map of weather conditions.