Human action recognition using variational Bayesian hidden Markov model with Gaussian-Wishart emission mixture model

In this paper, we proposed the human action recognition method using the variational Bayesian HMM with Gaussian — Wishart emission mixture model. First, we defined the Bayesian HMM based on a finite number of Gaussian-Wishart mixture components to support continuous emission observations. Second, we have considered a variational Bayesian inference method to derive the posterior distributions for hidden variables and parameters that are required to define the proposed model using training data. And then we have also derived the predictive distribution that is used to classify new action. Third, the human action classification using KTH data set has been conducted to evaluate the performance of proposed method. The experimental results showed that our method is more efficient with human action recognition than existing methods.