Combination of Video Change Detection Algorithms by Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Bianco:ieeeTEC,
  author =       "Simone Bianco and Gianluigi Ciocca and 
                 Raimondo Schettini",
  title =        "Combination of Video Change Detection Algorithms by
                 Genetic Programming",
  journal =      "IEEE Transactions on Evolutionary Computation",
  note =         "Accepted for future publication",
  keywords =     "genetic algorithms, genetic programming, Change
                 detection, algorithm combining and selection, CDNET.",
  ISSN =         "1089-778X",
  DOI =          "doi:10.1109/TEVC.2017.2694160",
  size =         "15 pages",
  abstract =     "Within the field of Computer Vision, change detection
                 algorithms aim at automatically detecting significant
                 changes occurring in a scene by analysing the sequence
                 of frames in a video stream. In this paper we
                 investigate how state-of-the-art change detection
                 algorithms can be combined and used to create a more
                 robust algorithm leveraging their individual
                 peculiarities. We exploited Genetic Programming (GP) to
                 automatically select the best algorithms, combine them
                 in different ways, and perform the most suitable
                 post-processing operations on the outputs of the
                 algorithms. In particular, algorithms combination and
                 post-processing operations are achieved with unary,
                 binary and n-ary functions embedded into the GP
                 framework. Using different experimental settings for
                 combining existing algorithms we obtained different GP
                 solutions that we termed IUTIS (In Unity There Is
                 Strength). These solutions are then compared against
                 state-of-the-art change detection algorithms on the
                 video sequences and ground truth annotations of the
                 Change Detection. net (CDNET 2014) challenge. Results
                 demonstrate that using GP, our solutions are able to
                 outperform all the considered single state-of-the-art
                 change detection algorithms, as well as other
                 combination strategies. The performance of our
                 algorithm are significantly different from those of the
                 other state-of-the-art algorithms. This fact is
                 supported by the statistical significance analysis
                 conducted with the Friedman Test and Wilcoxon Rank Sum
                 post-hoc tests.",
  notes =        "also known as \cite{7898824} See also
                 \cite{oai:arXiv.org:1505.02921}",
}

Genetic Programming entries for Simone Bianco Gianluigi Ciocca Raimondo Schettini

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