Contributo in atti di convegno, 2009, ENG, 10.1109/ICASSP.2009.4959945

Event recognition with time varying Hidden Markov Model

Wang Z.; Kuruoglu E. E.; Yang X.; Xu Y.; Yu S.

Shanghai Jiao Tong University, Shanghai, China; Shanghai Jiao Tong University, Shanghai, China; Shanghai Jiao Tong University, Shanghai, China; CNR-ISTI, Pisa, Italy

Standard Hidden Markov Model (HMM) and the more general Dynamic Bayesian Network (DBN) models assume stationarity of state transition distribution. However, this assumption does not hold for many real life events of interest. In this paper, we propose a new time sequence model that extends HMM to time varying scenario. The time varying property is realized in our model by explicitly allowing the change of state transition density as the time spent in a particular state passes by. Rather than keeping transition densities at different time spots independent of each other, we exploit their temporal correlation by applying a hierarchical Dirichlet prior. This leads to a more robust time varying model, especially when training data are scarce. We also employ Markov Chain Monte Carlo (MCMC) sampling in learning the MAP estimate of time varying parameters, with a transition kernel incorporating linear optimization. The proposed model is applied to recognizing real video events, and is shown to outperform existing HMM-based methods.

IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1761–1764, Taipei, Taiwan, 19-24 April 2009

Keywords

Bayesian networks, Time-varying hidden-Markov model, Event recognition

CNR authors

Kuruoglu Ercan Engin

CNR institutes

ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"

ID: 91980

Year: 2009

Type: Contributo in atti di convegno

Last update: 2018-02-12 09:07:50.000

External IDs

CNR OAI-PMH: oai:it.cnr:prodotti:91980

DOI: 10.1109/ICASSP.2009.4959945