Title :
Two-hand gesture recognition using coupled switching linear model
Author :
Kuno, Yoshinori ; Shimada, N.
Abstract :
We present a method of coupling multiple switching linear models. The coupled switching linear model is an interactive process of two switching linear models. Coupling is given through causal influence between their hidden discrete states. The parameters of this model are learned via EM algorithm. Tracking is performed through the coupled-forward algorithm based on Kalman filtering and a collapsing method. A model with maximum likelihood is selected out of a few learned models during tracking. We demonstrate the application of the proposed model to tracking and recognizing two-hand gestures.
Keywords :
Kalman filters; computer vision; gesture recognition; maximum likelihood estimation; probability; tracking; Coupled backward algorithm; EM algorithm; Kalman filtering; coupling multiple switching linear models; maximum likelihood estimation; model specification; probability density; tracking; two-hand gesture recognition; Active shape model; Ear; Equations; Hidden Markov models; Man machine systems; Mechanical systems; Shape control; Spline; Tracking; Vectors;
Conference_Titel :
Pattern Recognition, 2002. Proceedings. 16th International Conference on
Print_ISBN :
0-7695-1695-X
DOI :
10.1109/ICPR.2002.1044576