A semi-dynamic bayesian network for human gesture recognition

Myung Cheol Roh; Seong Whan Lee

doi:10.1109/ICSMC.2008.4811350

A semi-dynamic bayesian network for human gesture recognition

Research output: Contribution to journal › Conference article › peer-review

Abstract

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 language	English
Article number	4811350
Pages (from-to)	644-649
Number of pages	6
Journal	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
DOIs	https://doi.org/10.1109/ICSMC.2008.4811350
Publication status	Published - 2008
Event	2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008 - Singapore, Singapore Duration: 2008 Oct 12 → 2008 Oct 15

ASJC Scopus subject areas

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

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10.1109/ICSMC.2008.4811350

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title = "A semi-dynamic bayesian network for human gesture recognition",

abstract = "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.",

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AB - 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.

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A semi-dynamic bayesian network for human gesture recognition

Abstract

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