Automated multi-camera planar tracking correspondence modeling

This paper introduces a methodology for robustly estimating a planar tracking correspondence model (TCM) for a large camera network directly from tracking data and for using that model to track objects through multiple cameras. By exploiting the characteristics of tracking data, our method can robustly estimate a planar TCM in large environments covered by many cameras. It is robust to scenes with multiple simultaneously moving objects and limited visual overlap between the cameras. Our method introduces the capability of automatic calibration of large camera networks in which the topology of camera overlap is unknown and in which all cameras do not necessarily overlap. Results and numerical analysis are shown for a five camera network in which the topology is not specified.