  Visual sensor networks (VSNs) constitute a fundamental class of distributed
sensing systems, with unique complexity and appealing performance features,
which correspondingly bring in quite active lines of research. An important
research direction consists in the identification and estimation of the VSN
sensing features: these are practically useful when scaling with the number of
cameras or with the observed scene complexity. With this context in mind, this
paper introduces for the first time the idea of Stimulation Model (SM), as a
mathematical relation between the set of detectable events and the
corresponding stimulated cameras observing those events. The formulation of the
related SM identification problem is proposed, along with a proper network
observations model, and a solution approach based on deep embedded features and
soft clustering. In detail: first, the Gaussian Mixture Modeling is employed to
provide a suitable description for data distribution, while an autoencoder is
used to reduce undesired effects due to the so-called curse of dimensionality
emerging in case of large scale networks. Then, it is shown that a SM can be
learnt by solving Maximum A-Posteriori estimation on the encoded features
belonging to a space with lower dimensionality. Numerical results on synthetic
scenarios are reported to validate the devised estimation algorithm.
