In this work we present a consistent probabilistic approach to control multiple, but diverse pan-tilt-zoom cameras concertedly observing a scene. There are disparate goals to this control: the cameras are not only to react to objects moving about, arbitrating conflicting interests of target resolution and trajectory accuracy, they are also to anticipate the appearance of new targets. We base our control function on maximisation of expected mutual information gain, which to our knowledge is novel to the field of computer vision in the context of multiple pan-tilt-zoom camera control. This information theoretic measure yields a utility for each goal and parameter setting, making the use of physical or computational resources comparable. Weighting this utility allows to prioritise certain objectives or targets in the control. The resulting behaviours in typical situations for multi-camera systems, such as camera hand-off, acquisition of close-ups and scene exploration, are emergent but intuitive. We quantitatively show that without the need for hand crafted rules they address the given objectives.
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