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In this paper we deal with the multi-object tracking problem, with specific reference to the visual tracking of pedestrians, assuming that the pedestrian-detection step is already done. We use a Bayesian framework to combine the visual information provided by a simple image correlation algorithm with a behavioral model ( discrete choice model ) for pedestrian dynamic, calibrated on real data. We aim to show how the combination of the image information with a model of pedestrian behavior can provide appreciable results in real and complex scenarios.