Visual surveillance in wide areas (e.g. airports) relies on cameras that observe non-overlapping scenes. Multi-person tracking requires re-identification of a person when he/she leaves one field of view, and later appears at another. For this, we use appearance cues. Under the assumption that all observations of a single person are Gaussian distributed, the observation model in our approach consists of a Mixture of Gaussians. In this paper we propose a distributed approach for learning this MoG, where every camera learns from both its own observations and communication with other cameras. We present the multi-observations newscast EM algorithm for this, which is an adjusted version of the recently developed newscast EM. The presented algorithm is tested on artificial generated data and on a collection of real-world observations gathered by a system of cameras in an office building.