Knowledge based activity recognition with dynamic bayesian network

28Citations
Citations of this article
27Readers
Mendeley users who have this article in their library.

This article is free to access.

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. © 2010 Springer-Verlag.

Cite

CITATION STYLE

APA

Zeng, Z., & Ji, Q. (2010). Knowledge based activity recognition with dynamic bayesian network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6316 LNCS, pp. 532–546). Springer Verlag. https://doi.org/10.1007/978-3-642-15567-3_39

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free