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

Authors:
Kouji Miyazato;Akisato Kimura;Shigeru Takagi;Junji Yamato
Affiliations:
Department of Information and Communication Systems Engineering, Okinawa National College of Technology, Japan;NTT Communication Science Laboratories, NTT Corporation, Japan;Department of Information and Communication Systems Engineering, Okinawa National College of Technology, Japan;NTT Communication Science Laboratories, NTT Corporation, Japan
Venue:
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Year:
2009

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Abstract

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.