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dc.contributorEscalera Guerrero, Sergio
dc.contributorBaró Solé, Xavier
dc.contributor.authorPonce López, Víctor
dc.date.accessioned2012-09-25T08:53:20Z
dc.date.available2012-09-25T08:53:20Z
dc.date.issued2012-06-22
dc.identifier.urihttp://hdl.handle.net/2099.1/16087
dc.description.abstractIn this M. Sc. Thesis, we deal with the problem of Human Gesture Recognition using Human Behavior Analysis technologies. In particular, we apply the proposed methodologies in both health care and social applications. In these contexts, gestures are usually performed in a natural way, producing a high variability between the Human Poses that belong to them. This fact makes Human Gesture Recognition a very challenging task, as well as their generalization on developing technologies for Human Behavior Analysis. In order to tackle with the complete framework for Human Gesture Recognition, we split the process in three main goals: Computing multi-modal feature spaces, probabilistic modelling of gestures, and clustering of Human Poses for Sub-Gesture representation. Each of these goals implicitly includes different challenging problems, which are interconnected and faced by three presented approaches: Bag-of-Visual-and-Depth-Words, Probabilistic-Based Dynamic Time Warping, and Sub-Gesture Representation. The methodologies of each of these approaches are explained in detail in the next sections. We have validated the presented approaches on different public and designed data sets, showing high performance and the viability of using our methods for real Human Behavior Analysis systems and applications. Finally, we show a summary of different related applications currently in development, as well as both conclusions and future trends of research.
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subject.lcshPattern recognition systems
dc.subject.lcshBody language
dc.titleMulti-modal human gesture recognition combining dynamic programming and probabilistic methods
dc.typeMaster thesis
dc.subject.lemacReconeixement de formes (Informàtica)
dc.subject.lemacLlenguatge corporal
dc.rights.accessOpen Access
dc.audience.educationlevelMàster
dc.audience.mediatorFacultat d'Informàtica de Barcelona
dc.audience.degreeMÀSTER UNIVERSITARI EN INTEL·LIGÈNCIA ARTIFICIAL (Pla 2009)


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