A key problem in widely distributed camera networks is geolocating the cameras. In this work we consider three scenarios for camera localization: localizing a camera in an unknown environment using the diurnal cycle, localizing a camera using weather variations by finding correlations with satellite imagery, and adding a new camera in a region with many other cameras.
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 the AMOS dataset and find that for cameras that remain stationary and for which we have accurate image timestamps, we can localize most cameras to within 50 miles of the known location.
This project is supported under NSF IIS 0546383: "CAREER: Passive Vision, What Can Be Learned by a Stationary Observer". Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.