Track-Person Association Using a First-Order Probabilistic Model

This work addresses the problem of track association in person tracking. We propose a probabilistic model, based on Markov Logic Networks, that aims at associating the individual tracks emerging from a person tracking algorithm to the correct persons. For this purpose the continuous estimates of the object positions acquired by the tracking algorithm are mapped into discrete spatial regions, which are based on a floor plan of the environment. Experiments show that the described model is able to exploit the additional information contained inside the provided floor plan, and deliver good results compared to a state of the art person tracking algorithm despite the lossy discretization step. We discuss the engineered model in detail and give an empirical evaluation using an indoor setting.