Extending evolutionary Fuzzy Quantile Inference to classify partially occluded human motions

Created by W.Langdon from gp-bibliography.bib Revision:1.4394

  author =       "Mehdi Khoury and Honghai Liu",
  title =        "Extending evolutionary Fuzzy Quantile Inference to
                 classify partially occluded human motions",
  booktitle =    "IEEE International Conference on Fuzzy Systems
                 (FUZZ-IEEE 2010)",
  year =         "2010",
  address =      "Barcelona, Spain",
  month =        "18-23 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-4244-6920-8",
  abstract =     "This work presents a framework that combines the
                 concept of Fuzzy Quantile Inference(FQI) with Genetic
                 Programming (GP) in order to accurately classify real
                 natural 3d human Motion Capture data. FQI is a
                 generalisation of Fuzzy Gaussian Inference. It builds
                 Fuzzy Membership Functions that map to hidden
                 Probability Distributions underlying human motions,
                 providing a suitable modelling paradigm for such noisy
                 data. Genetic Programming (GP) is used to make a time
                 dependent and context aware filter that improves the
                 qualitative output of the classifier. Results show that
                 FQI outperforms a GMM-based classifier when recognising
                 six different boxing stances simultaneously, and that
                 the addition of the GP based filter improves the
                 accuracy of the FQI classifier significantly. A
                 mechanism allowing the FQI extended framework to deal
                 with occluded data reasonably well is also
  DOI =          "doi:10.1109/FUZZY.2010.5584623",
  notes =        "WCCI 2010. Also known as \cite{5584623}",

Genetic Programming entries for Mehdi Khoury Honghai Liu