Modeling people's focus of attention

In this paper, we present an approach to model focus of attention of participants in a meeting via hidden Markov models (HMM). We employ HMM to encode and track focus of attention, based on the participants' gaze information and knowledge of their positions. The positions of the participants are detected by face tracking in the view of a panoramic camera mounted on the meeting table. We use neural networks to estimate the participants' gaze from camera images. We discuss the implementation of the approach in detail, including system architecture, data collection, and evaluation. The system has achieved an accuracy rate of up to 93% in detecting focus of attention on test sequences taken from meetings. We have used focus of attention as an index in a multimedia meeting browser.