Title :
A statistical upper body model for 3D static and dynamic gesture recognition from stereo sequences
Author :
Nefian, Ara V. ; Grzeszczuk, Radek ; Eruhimov, Victor
Author_Institution :
Microprocessor Res. Labs., Intel Corp., Santa Clara, CA, USA
fDate :
6/23/1905 12:00:00 AM
Abstract :
This paper describes a hidden Markov model-based static and dynamic 3D gesture recognition system. The shape and position of the hands, segmented and tracked using a novel 3D statistical model for the upper body in stereo sequences, are used as observation vectors. The upper body model allows for accurate 3D localization of the hands in the presence of partial occlusions, self occlusions and different illumination conditions. The accuracy of our approach is reflected by the performance of our 3D gesture based editing system, that reaches 96% over 12 dynamic gestures and four static gestures
Keywords :
gesture recognition; hidden Markov models; image segmentation; image sequences; parameter estimation; statistical analysis; stereo image processing; 3D dynamic gesture recognition; 3D gesture based editing system; 3D localization; 3D static gesture recognition; 3D statistical model; HMM; arm segmentation; hand segmentation; head segmentation; hidden Markov model; illumination conditions; observation vectors; parameter estimation; partial occlusions; self occlusions; statistical upper body model; stereo sequences; torso segmentation; upper body model; Application software; Cameras; Hidden Markov models; Humans; Image segmentation; Lighting; Microprocessors; Pixel; Robustness; Shape;
Conference_Titel :
Image Processing, 2001. Proceedings. 2001 International Conference on
Conference_Location :
Thessaloniki
Print_ISBN :
0-7803-6725-1
DOI :
10.1109/ICIP.2001.958107
