Hidden Markov Model on a unit hypersphere space for gesture trajectory recognition

Authors:
Jounghoon Beh;David K. Han;Ramani Durasiwami;Hanseok Ko
Affiliations:
University of Maryland Institute for Advanced Computer Studies, United States;Office of Naval Research, United States;University of Maryland Institute for Advanced Computer Studies, United States;University of Maryland Institute for Advanced Computer Studies, United States and School of Electrical Engineering, Korea University, Seoul, Republic of Korea
Venue:
Pattern Recognition Letters
Year:
2014

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Abstract

In this paper, a Mixture of von Mises-Fisher (MvMF) Probability Density Function (PDF) is incorporated into a Hidden Markov Model (HMM) in order to model spatio-temporal data in a unit-hypersphere space. The parameter estimation formulae for MvMF-HMM are derived in a closed form. As an application for the proposed MvMF-HMM, hands gesture trajectory recognition task is considered. Modeling gesture trajectory on a unit-hypersphere inherently removes bias from a subject's arm length or distance between a subject and camera. In experiments with public datasets, InteractPlay and UCF Kinect, the proposed MvMF-HMM showed superior recognition performance compared to current state-of-the-art techniques.