Nonlinear PHMMs for the Interpretation of Parameterized Gesture

In previous work [14], we modify the hidden Markov model (HMM) framework to incorporate a global parametric variation in the output probabilities of the states of the HMM. Development of the parametric hidden Markov model (PHMM)wasmotivatedbythetaskof simultaneously recognizing and interpreting gestures that exhibit meaningful variation. With standard HMMs, such global variation confounds the recognition process. The original PHMM approach assumes a linear dependence of output density means on the global parameter. In this paper we extend the PHMM to handle arbitrary smooth (nonlinear) dependencies. We show a generalized expectation-maximization (GEM) algorithmfor trainingthe PHMM and a GEM algorithm to simultaneously recognize the gesture and estimate the value of the parameter. We present results on a pointing gesture, where the nonlinear approach permits the natural azimuth/elevation parameterization of pointing direction.

• Type: Conference
• Year: 1998
• Where: CVPR
• Authors: Andrew D. Wilson, Aaron F. Bobick
• Keywords: Computer Vision, CVPR 1998, Global Parametric Variation, Global Variation, Hidden Markov Model, Parametric Hidden Markov, Pointing Gesture