A Multi-Camera Multi-Target Tracker based on Factor Graphs

System modeling with Probabilistic Graphical Models (PGM) has become increasingly popular in the last years. In this paper we design a Multiple Target Tracker based on the probabilistic architecture of Normal Factor Graph. Belief propagation makes best use of data coming from different branches of the graph and yields the tracks via messages fusion. The issues of data association, track life-cycle management and data fusion from heterogeneous sensor modalities are resolved at each time step by propagating and combining forward and backward probabilistic messages. Inexpensive cameras deployed in the scene under surveillance are the primary sensor modality, even if the framework has been designed to receive data from a wide range of sensors such as Radars, Infrared cameras, etc. The framework has been tested by calculating the tracks of different ships moving in an harbour framed by three cameras.