An ICA Mixture Hidden Conditional Random Field Model for Video Event Classification

In this paper, a hidden conditional random field (HCRF) model with independent component analysis (ICA) mixture feature functions is developed for video event classification. Video content analysis problems can be modeled using graphical models. The hidden Markov model (HMM) is a commonly used graphical model, but the HMM has several limitations such as the assumption of observation independence, the form of observation distribution and the Markov chain interaction. Unlike the HMM, the HCRF is a discriminative model without conditional independence assumption of observations, and is more suitable for video content analysis. We formulate the video content analysis problem using a new HCRF framework based on the temporal interactions between video frames. In addition, according to the non-Gaussian property of video event features, a new feature function using the likelihoods of ICA mixture components is proposed for local observation to further enhance the HCRF model. The discriminative power of the HCRF and representation power of the ICA mixture for non-Gaussian distributions are combined in the new model. The new model is applied to the challenging bowling and golf event classifications as case studies. The simulation results support the analysis that the new ICA mixture HCRF (ICAMHCRF) outperforms the existing mixture HMM models in terms of classification accuracy.