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

• Main Author: Brown, Dane
• Other Authors: Ghaziasgar, Mehrdad
• Language: en
• Published: University of Western Cape 2014
• Subjects: Pose recognition and estimation; Graphics processing unit; Compute unified device architecture; Face detection; Skin detection; Background subtraction; Morphological operations; Haar features; Support vector machine; Blender
• Online Access: http://hdl.handle.net/11394/3455

>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.