Euclidean Camera Calibration Using GPS Side InformationGerald DalleyIntroductionConsider the problem of wide-area surveillance, such as traffic monitoring and activity classification around critical assets (e.g. an embassy, a troop base, critical infrastructure facilities such as oil depots, port facilities, airfield tarmacs). We want to monitor the flow of movement in such a setting from a large number of cameras, typically with non-overlapping fields of view. To coordinate the observations in these distributed cameras, we need to know the relative locations of the fields of view (e.g. what portion of the earth's surface does each camera see). In some instances, one can carefully site and calibrate the cameras to manually obtain a mapping from the camera pixel coordinates to latitude, longitude coordinates on the earth's surface. However, in many cases, cameras must be rapidly deployed and may not last for long periods of time. Additionally, even carefully calibrated cameras tend to move after being bumped or even rattled by large passing vehicles; new cameras tend to be added to systems over time, and older cameras fail. Additionally, outfitting the camera itself with a global positioning system (GPS) receiver is a suboptimal solution. First, many cameras are mounted on the sides of large structures such as buildings that block and/or distort GPS readings. Secondly, a GPS receiver located at the camera indicates where the camera is, not where the groundplane that it views is located. GPS Side InformationFor our project, we assume that we have an installed network of cameras and at least one object moving through the surveillance area that is instrumented with a GPS receiver. Note that we do not have correspondence between the instrumented objects and camera observations, e.g. when we see an object pass through a camera, we do not know to which, if any, instrumented object it corresponds. Under this setup, we know the latitude and longitude of each instrumented
object at each point in time. We denote this data as the set where
We separately have access to the recorded in-camera tracking data that
reports when vehicles enter and exit each camera's field-of-view: Although we cannot quite estimate where ε is a hidden factor that indicates how well To test this algorithm, we use a dataset consisting of five cameras,
five instrumented vehicles following scripted behavior, and approximately
unplanned 17 vehicles that passed through the cameras during the data
collection period. In the below figure, we show
Future WorkWe have several areas where we are working on improving and extending these preliminary results:
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