The calibration of an unknown sensor, such as a camera, is a key issue in the sensor fusion domain. This paper addresses this problem by expanding upon previously introduced work. This method uses a unified Bayesian framework with an alternative parameterisation known as disparity space to calibrate an unknown camera's spatial parameters in reference to a known camera. Here, the recently developedLinear-Complexity Cumulant (LCC) filter is used to improve the both the multitarget tracking and calibration facets of the framework. The new implementation is compared against a Probability Hypothesis Density (PHD) method upon simulated data.