Real-time tracking of single people and groups simultaneously by contextual graph-based reasoning dealing complex occlusions

In this paper we present a real-time tracking algorithm able to follow simultaneously single objects and groups of objects. The proposed method is an improvement of the approach that we recently proposed in [1], able to exploit the history of moving objects by means of a Finite State Automaton. The main novelty of the proposed method refers to the strategy used to associate the evidence at the current frame to the objects tracked in the previous one. This strategy is able to take into account only the possible feasible combinations by means of an efficient and robust graph-based approach, which exploit the spatio-temporal continuity of moving objects. The method has been compared over a standard dataset with the participants to the international PETS 2010 contest, confirming good efficiency and generality.