A semi-dynamic bayesian network for human gesture recognition

Myung Cheol Roh, Seong Whan Lee

Research output: Contribution to journalConference articlepeer-review


Many methods for human gesture recognition have been researched. Bayesian Network (BN) and Dynamic Bayesian Network (DBN) are representative powerful tools for the gesture recognition. However, conventional BN is not appropriate in sequential data, and conventional DBN does not always guarantee that a sequence has relatively higher probability in a true class than in other classes. Moreover, the complexity of the DBN is increased exponentially with increasing number of hidden nodes and large number of training data is needed to guarantee the performance. Therefore, we propose a Semi-DBN (Semi-Dynamic Bayesian Network) which outperforms the conventional BNs and DBNs while it requires much less computational cost.

Original languageEnglish
Article number4811350
Pages (from-to)644-649
Number of pages6
JournalConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Publication statusPublished - 2008
Event2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008 - Singapore, Singapore
Duration: 2008 Oct 122008 Oct 15

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Human-Computer Interaction


Dive into the research topics of 'A semi-dynamic bayesian network for human gesture recognition'. Together they form a unique fingerprint.

Cite this