Understanding Human Action using a Bayesian HMM with a Continuous Gaussian-Wishart Emission Model

Abstract

Understanding human action has become a popular issue in artificial intelligence. To address this, we propose combining a Bayesian hidden Markov model (HMM) with a continuous Gaussian-Wishart emission model. First, we define the HMM with the Gaussian-Wishart emission mixture model to express a sequence of continuous observation feature vectors from a human action video. Second, we use an approximated variational Bayesian inference to derive posterior distributions for hidden variables and parameters required to define the proposed model. Third, we derive the approximated predictive distribution for an observation sequence that can be used to represent a new action, and then, we compute a likelihood function that indicates the probability that the new observation sequence belongs to each class using the approximated predictive distribution. Fourth, we classify the new action into a human action category that maximizes the likelihood function value. Finally, to evaluate the performance of the proposed method, we conduct human action classification using a KTH human action dataset. The experimental results show that the recognition rate with our method is in the middle position among various existing methods.