Study of GP representations for motion detection with unstable background

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Andy Song and Brian Pinto",
  title =        "Study of GP representations for motion detection with
                 unstable background",
  booktitle =    "IEEE Congress on Evolutionary Computation (CEC 2010)",
  year =         "2010",
  address =      "Barcelona, Spain",
  month =        "18-23 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-4244-6910-9",
  abstract =     "Detecting moving objects is a significant component in
                 many machine vision systems. One of the challenges in
                 real world motion detection is the unstability of the
                 background. An ideal method is expected to reliably
                 detect interesting movements from videos while ignoring
                 background/uninteresting movements. In this paper,
                 Genetic Programming (GP) based motion detection method
                 is used to tackle this issue, as it is a powerful
                 learning method and has been successfully applied on
                 various image analysis tasks. The investigation here
                 focuses on the various representations of GP for motion
                 detection and the suitability of these approaches. The
                 unstable environments in this study include ripples on
                 river, rainy background and moving cameras. It can be
                 shown from the results that with a suitable frame
                 representation and function set, reliable GP programs
                 can be evolved to handle complex unstable background.",
  DOI =          "doi:10.1109/CEC.2010.5586334",
  notes =        "WCCI 2010. Also known as \cite{5586334}",

Genetic Programming entries for Andy Song Brian Pinto