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Real-time estimation of human visual attention with dynamic Bayesian network and MCMC-based particle filter

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4 Author(s)
Miyazato, K. ; Dept. of Inf. & Commun. Syst. Eng., Okinawa Nat. Coll. of Technol., Okinawa, Japan ; Kimura, A. ; Takagi, S. ; Yamato, J.

Recent studies in signal detection theory suggest that the human responses to the stimuli on a visual display are nondeterministic. People may attend to different locations on the same visual input at the same time. Constructing a stochastic model of human visual attention would be promising to tackle the above problem. This paper proposes a new method to achieve a quick and precise estimation of human visual attention based on our previous stochastic model with a dynamic Bayesian network. A particle filter with Markov chain Monte-Carlo (MCMC) sampling make it possible to achieve a quick and precise estimation through stream processing. Experimental results indicate that the proposed method can estimate human visual attention in real time and more precisely than previous methods.

Published in:

Multimedia and Expo, 2009. ICME 2009. IEEE International Conference on

Date of Conference:

June 28 2009-July 3 2009