Modelling pedestrian trajectory patterns with Gaussian processes

David Ellis, Eric Sommerlade, Ian Reid
2009 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops  
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 the
more » ... it of this approach for long term motion prediction where parametric models such as Kalman Filters would perform poorly.
doi:10.1109/iccvw.2009.5457470 dblp:conf/iccvw/EllisS009 fatcat:twq3a2i4c5ezrejd65rdbpymfi