Visual tracking of highly articulated objects using massively parallel processors

Hand gesture recognition has the potential of simplifying human computer interactions. However, the human hand is a highly articulated object, capable of taking on many different appearances. In this work, we consider an analysis by synthesis approach to this difficult tracking problem. We attempt to overcome the vast amount of computation required by implementing the algorithm on commodity GPUs. We also collect a lengthy sequence of hand motions from five cameras in order to train and test our algorithm. We show that to achieve good tracking performance, it is important to understand the way that the hand moves. It is of secondary importance to have a good estimate of the hand shape and to be able to process the frames as quickly as possible. Under heavily controlled circumstances, we are able to achieve full tracking accuracy.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2012

Author dc:creator

• Lin, Dennis

Contributors dc:contributor

• Huang, Thomas S.
• Ahuja, Narendra
• Forsyth, David A.
• Hwu, Wen-Mei W.

• computer vision
• articulated model
• tracking
• hand gesture

Statement dc:rights

• Copyright 2011 Dennis Lin

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/29829

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/29829