Learning dynamic Bayesian network discriminatively for human activity recognition

The purpose of this paper is to develop an approach to learn dynamic Bayesian network (DBN) discriminatively for human activity recognition. DBN is a generative model widely used for modeling temporal events in human activity recognition. The parameters of the DBN models are usually learned through maximizing likelihood or expected likelihood. However, activity is often recognized through identifying the activity class with the highest posterior probability. Hence, there is discrepancy between the learning and classification criteria. In this paper, we focus on developing a discriminative parameter learning approach for hybrid DBNs that has a consistent criterion during training and testing. Our approach is applicable to parameter learning with both complete data and incomplete data, and empirical studies show the proposed discriminative learning approach outperforms the maximum likelihood or EM algorithm in activity recognition tasks.