Abstract

In this talk, I will talk about the possibility of using Bayesian nonparametric clustering, or Dirichlet Process Mixture model to solve human activity recognition problem. In particular, I will discuss how the technique can be useful when the activity labels are not annotated and/or the activity evolves over the time. This initial study is built on an existing work on using directional statistical models (von Mises-Fisher) distribution, called Hierarchical Mixture of Conditional Independent von Mises Fisher distribution (HMCIvMFs), for unknown events detection and learning. Markov chain Monte Carlo sampling based learning algorithm will be presented together with some initial experiment results.