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Faster upper body pose recognition and estimation using compute unified device architecture

>Magister Scientiae - MSc / The SASL project is in the process of developing a machine translation system that can
translate fully-fledged phrases between SASL and English in real-time. To-date, several
systems have been developed by the project focusing on facial expression, hand shape,
hand motion, hand orientation and hand location recognition and estimation. Achmed
developed a highly accurate upper body pose recognition and estimation system. The
system is capable of recognizing and estimating the location of the arms from a twodimensional video captured from a monocular view at an accuracy of 88%. The system operates at well below real-time speeds. This research aims to investigate the use of optimizations and parallel processing techniques using the CUDA framework on Achmed’s algorithm to achieve real-time upper body pose recognition and estimation. A detailed analysis of Achmed’s algorithm identified potential improvements to the algorithm. Are- implementation of Achmed’s algorithm on the CUDA framework, coupled with these improvements culminated in an enhanced upper body pose recognition and estimation system that operates in real-time with an increased accuracy.

Identiferoai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uwc/oai:etd.uwc.ac.za:11394/3455
Date January 2013
CreatorsBrown, Dane
ContributorsGhaziasgar, Mehrdad, James Connan, James
PublisherUniversity of Western Cape
Source SetsSouth African National ETD Portal
LanguageEnglish
Detected LanguageEnglish
TypeThesis
RightsUniversity of Western Cape

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