Knowledge Based Activity Recognition with Dynamic Bayesian Network

Abstract

In this paper, we propose solutions on learning dynamic Bayesian network (DBN) with domain knowledge for human activity recognition. Different types of domain knowledge, in terms of first order probabilistic logics (FOPLs), are exploited to guide the DBN learning process. The FOPLs are transformed into two types of model priors: structure prior and parameter constraints. We present a structure learning algorithm, constrained structural EM (CSEM), on learning the model structures combining the training data with these priors. Our method successfully alleviates the common problem of lack of sufficient training data in activity recognition. The experimental results demonstrate simple logic knowledge can compensate effectively for the shortage of the training data and therefore reduce our dependencies on training data.

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