  In modern production facilities industrial robots and humans are supposed to
interact sharing a common working area. In order to avoid collisions, the
distances between objects need to be measured conservatively which can be done
by a camera network. To estimate the acquired distance, unmodelled objects,
e.g., an interacting human, need to be modelled and distinguished from
premodelled objects like workbenches or robots by image processing such as the
background subtraction method.
  The quality of such an approach massively depends on the settings of the
camera network, that is the positions and orientations of the individual
cameras. Of particular interest in this context is the minimization of the
error of the distance using the objects modelled by the background subtraction
method instead of the real objects. Here, we show how this minimization can be
formulated as an abstract optimization problem. Moreover, we state various
aspects on the implementation as well as reasons for the selection of a
suitable optimization method, analyze the complexity of the proposed method and
present a basic version used for extensive experiments.
