Automatic pedestrian tracking using discrete choice models and image correlation techniques

  • Authors:

  • Santiago Venegas-Martinez;Gianluca Antonini;Jean Philippe Thiran;Michel Bierlaire

  • Affiliations:

  • Signal Processing Institute (ITS), Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland;Signal Processing Institute (ITS), Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland;Signal Processing Institute (ITS), Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland;Michel Bierlaire, Operation Research Group ROSO, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland

  • Venue:
  • MLMI'04 Proceedings of the First international conference on Machine Learning for Multimodal Interaction
  • Year:
  • 2004

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Abstract

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.