Consistent labeling of tracked objects in multiple cameras with overlapping fields of view
Abbreviated Journal Title
IEEE Trans. Pattern Anal. Mach. Intell.
tracking; multiple cameras; multiperspective video; surveillance; camera; handoff; sensor fusion; Computer Science, Artificial Intelligence; Engineering, Electrical &; Electronic
In this paper, we address the issue of tracking moving objects in an environment covered by multiple uncalibrated cameras with overlapping fields of view, typical of most surveillance setups. In such a scenario, it is essential to establish correspondence between tracks of the same object, seen in different cameras, to recover complete information about the object. We call this the problem of consistent labeling of objects when seen in multiple cameras. We employ a novel approach of finding the limits of field of view (FOV) of each camera as visible in the other cameras. We show that, if the FOV lines are known, it is possible to disambiguate between multiple possibilities for correspondence. We present a method to automatically recover these lines by observing motion in the environment. Furthermore, once these lines are initialized, the homography between the views can also be recovered. We present results on indoor and outdoor sequences containing persons and vehicles.
Ieee Transactions on Pattern Analysis and Machine Intelligence
"Consistent labeling of tracked objects in multiple cameras with overlapping fields of view" (2003). Faculty Bibliography 2000s. 3854.