Over the last years, the rapid growth of distributed smart cameras has triggered the search for new approaches of smart-ness of cameras to have better results. As communication among camera entities is becoming more and more complex and new ways of modeling communication have been proposed. These new ways have been taking inspiration from different fields such as socioeconomic approach or game theory. Moreover, one of the major problems of the camera network is re-identification. However, in most cases, the interaction between cameras presupposes that the latter are able to perform perfect and unambiguous detections, thus limiting the decision tasks to the Markovian model. Within this paper, we present a new approach of interaction between cameras based on a non-Markovian model. To resolve this issue, we can exploit other types of information rather than visual information to improve re-identification. This information is Spatial, Visual and Temporal (SVT). Temporal information holds the time needed to go from one camera to another, while spatial information contains the path followed by the target which is a key point for the decision-making process. This offers the possibility for the network to learn regularities and then reach a steady state.