Evolutionary Approximation of Gradient Orientation Module in HOG-based Human Detection System

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Michal Wiglasz and Lukas Sekanina",
  title =        "Evolutionary Approximation of Gradient Orientation
                 Module in {HOG-based} Human Detection System",
  booktitle =    "2017 IEEE Global Conference on Signal and Information
                 Processing GlobalSIP 2017",
  year =         "2017",
  editor =       "Z. Jane Wang and Costas Kotropoulos and Qionghai Dai",
  pages =        "1300--1304",
  pages =        "SAC--P.1.10",
  address =      "Montreal",
  month =        nov # " 14–16",
  publisher =    "IEEE Signal Processing Society",
  keywords =     "genetic algorithms, genetic programming, Cartesian
                 genetic programming, functional approximation,
                 Histogram of oriented gradients: Poster",
  isbn13 =       "978-1-5090-5989-8",
  language =     "english",
  URL =          "http://www.fit.vutbr.cz/research/view_pub.php?id=11441",
  URL =          "http://www.fit.vutbr.cz/~iwiglasz/pubs.php.en?id=11441&yfile=%2Fpub%2F11441%2F0001300.pdf",
  DOI =          "doi:10.1109/GlobalSIP.2017.8309171",
  size =         "5 page",
  abstract =     "The histogram of oriented gradients (HOG) feature
                 extraction is a computer vision method widely used in
                 embedded systems for detection of objects such as
                 pedestrians. We used Cartesian genetic programming
                 (CGP) to exploit the error resilience in the HOG
                 algorithm. We evolved new approximate implementations
                 of the arctangent function, which is typically employed
                 to compute the gradient orientations. When the best
                 evolved approximations are integrated into the SW
                 implementation of the HOG algorithm, not only the
                 execution time, but also the classification accuracy
                 was improved in comparison with the accurate
                 implementation and the state-of-the art approximate
  notes =        "https://2017.ieeeglobalsip.org/Papers/PublicSessionIndex3.asp?Sessionid=1063
                 Also known as \cite{8309171}",

Genetic Programming entries for Michal Wiglasz Lukas Sekanina