Mining activities using sticky multimodal dual hierarchical Dirichlet process hidden Markov model

In this paper, a new nonparametric Bayesian model called Sticky Multimodal Dual Hierarchical Dirichlet Process Hidden Markov Model (SMD-HDP-HMM) is proposed for mining activities from a collection of time series. An activity is modeled as an HMM where each state corresponds to an atomic activity. By extensively using Dirichlet Process (DP), multiple HMMs sharing a common set of states are learned and the numbers of HMMs and states are both automatically determined. Each time series is modeled to be generated by one of the HMMs such that all time series are clustered into activities. Simultaneously state sequences for time series are learned and each of them is decomposed into a sequence of atomic activities. Experimental results on KTH activity dataset demonstrate the advantage of our method.