This work presents a method to control multiple, but diverse pan-tilt-zoom cameras which are sharing overlapping views of the same spatial location for the purpose of observation of this scene. We cast this control input selection problem in an information-theoretic framework, where we maximise the expected mutual information gain in the scene model with respect to the observation parameters. The scene model yielding this information comprises several dynamic targets, augmented by one which has not yet been detected. The information content of the former is supplied directly by the uncertainties computed using a Sequential Kalman Filter tracker for the observed targets, while the undetected is modelled using a Poisson process for every element of a common ground plane. Together these yield an informationtheoretic utility for each parameter setting for each camera, triggering collaborative explorative behaviour of the system. Overall this yields a framework in which heterogeneous active camera types can be integrated cleanly and consistently, obviating the need for a wide-angle supervisor camera or other artificial restrictions on the camera parameter settings.