Bayesian integration of a discrete choice pedestrian behavioral model and image correlation techniques for automatic multiobject tracking
In this paper we deal with the multiobject tracking problem in the particular case of pedestrians, assuming the detection step already done. We use a Bayesian framework to combine the likelihood term provided by an image correlation algorithm with a prior distribution given by a discrete choice model for pedestrian behavior, calibrated on real data. We aim to show how the combination of the image information with a model of pedestrian behavior can provides appreciable results in real and complex scenarios.