Enhanced Simultaneous Camera Calibration and Path Estimation

This paper addresses two issues related to the simultaneous calibration of a network of imaging sensors and the recovery of the trajectory of a single target moving among them. The non-overlapping fields of view for the cameras do not cover the entire scene, resulting in times for which no measurements are available. A Bayesian framework is imposed on the problem in order to compute the MAP (maximum a posteriori) estimate for both the trajectory of the target and the translation and rotation of each camera within the global scene. First, three model order reduction techniques that decrease the dimension of the search space and the number of terms in the objective function are presented, thereby reducing the computational requirements of the search algorithm used to solve the optimization problem. Next, the problem of finding a solution that is consistent with the set of observation times is addressed, so that the target's estimated state does not fall within the field of view of the sensor network at a time for which no measurement is available. Three techniques that treat the missing measurements as additional inequality or equality constraints within the MAP optimization framework are presented.