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We propose a non-parametric model for pedestrian motion based on Gaussian Process regression, in which trajectory data are modelled by regressing relative motion against current position. We show how the underlying model can be learned in an unsupervised fashion, demonstrating this on two databases collected from static surveillance cameras. We furthermore exemplify the use of model for prediction, comparing the recently proposed GP-Bayesfilters with a Monte Carlo method. We illustrate thedoi:10.1109/iccvw.2009.5457470 dblp:conf/iccvw/EllisS009 fatcat:twq3a2i4c5ezrejd65rdbpymfi