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Knowledge Based Activity Recognition with Dynamic Bayesian Network

  • Zhi Zeng
  • Qiang Ji
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6316)

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.

Keywords

Domain Knowledge Training Sequence Dynamic Bayesian Network Candidate Structure Defense Advance Research Project Agency 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Zhi Zeng
    • 1
  • Qiang Ji
    • 1
  1. 1.Rensselaer Polytechnic InstituteTroyUSA

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