A Semi-Dynamic Bayesian Network for human gesture recognition

Myung-Cheol Roh, Seong-Whan Lee

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.