A fuzzy probabilistic inference methodology for constrained 3D human motion classification

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

@PhdThesis{mehdi_khoury_thesis_2010,
  author =       "Mehdi Khoury",
  title =        "A fuzzy probabilistic inference methodology for
                 constrained 3D human motion classification",
  school =       "University of Portsmouth",
  year =         "2010",
  address =      "UK",
  month =        "6 " # oct,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://eprints.port.ac.uk/1668/1/mehdi_khoury_thesis_2010.pdf",
  URL =          "http://eprints.port.ac.uk/id/eprint/1668",
  URL =          "http://ethos.bl.uk/OrderDetails.do?did=1&uin=uk.bl.ethos.523618",
  size =         "159 pages",
  abstract =     "Enormous uncertainties in unconstrained human motions
                 lead to a fundamental challenge that many recognising
                 algorithms have to face in practice: efficient and
                 correct motion recognition is a demanding task,
                 especially when human kinematic motions are subject to
                 variations of execution in the spatial and temporal
                 domains, heavily overlap with each other,and are
                 occluded. Due to the lack of a good solution to these
                 problems, many existing methods tend to be either
                 effective but computationally intensive or efficient
                 but vulnerable to misclassification.

                 This thesis presents a novel inference engine for
                 recognising occluded 3D human motion assisted by the
                 recognition context. First, uncertainties are wrapped
                 into a fuzzy membership function via a novel method
                 called Fuzzy Quantile Generation which employs metrics
                 derived from the probabilistic quantile function. Then,
                 time-dependent and context-aware rules are produced via
                 a genetic programming to smooth the qualitative outputs
                 represented by fuzzy membership functions. Finally,
                 occlusion in motion recognition is taken care of by
                 introducing new procedures for feature selection and
                 feature reconstruction.

                 Experimental results demonstrate the effectiveness of
                 the proposed framework on motion capture data from real
                 boxers in terms of fuzzy membership generation,
                 context-aware rule generation, and motion occlusion.
                 Future work might involve the extension of Fuzzy
                 Quantile Generation in order to automate the choice of
                 a probability distribution, the enhancement of temporal
                 pattern recognition with probabilistic paradigms, the
                 optimisation of the occlusion module, and the
                 adaptation of the present framework to different
                 application domains.",
  notes =        "Dr Honghai Liu

                 PySTEP or Python Strongly Typed gEnetic Programming
                 Open Source package

                 EThOS ID: uk.bl.ethos.523618",
}

Genetic Programming entries for Mehdi Khoury

Citations