Behavioral Priors for Detection and Tracking of Pedestrians in Video Sequences

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

  • Affiliations:
  • Ecole Polytechnique Federale de Lausanne (EPFL), Signal Processing Institute (STI/ITS/LTS5), Lausanne, Switzerland CH-1015;Ecole Polytechnique Federale de Lausanne (EPFL), Signal Processing Institute (STI/ITS/LTS5), Lausanne, Switzerland CH-1015;Ecole Polytechnique Federale de Lausanne (EPFL), Operation Research, Lausanne, Switzerland CH-1015;Ecole Polytechnique Federale de Lausanne (EPFL), Signal Processing Institute (STI/ITS/LTS5), Lausanne, Switzerland CH-1015

  • Venue:
  • International Journal of Computer Vision
  • Year:
  • 2006

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Abstract

In this paper we address the problem of detection and tracking of pedestrians in complex scenarios. The inclusion of prior knowledge is more and more crucial in scene analysis to guarantee flexibility and robustness, necessary to have reliability in complex scenes. We aim to combine image processing methods with behavioral models of pedestrian dynamics, calibrated on real data. We introduce Discrete Choice Models (DCM) for pedestrian behavior and we discuss their integration in a detection and tracking context. The obtained results show how it is possible to combine both methodologies to improve the performances of such systems in complex sequences.