Abstract-The visual focus of attention (VFOA) has been recognized as a prominent conversational cue. We are interested in the VFOA tracking of a group of people involved in social interaction. We note that in this case the participants look either at each other or at an object of interest; therefore they don't always face a camera and, consequently, their gazes (and their VFOAs) cannot be based on eye detection and tracking. We propose a method that exploits the correlation between gaze direction and head orientation. Both VFOA and gaze are modeled as latent variables in a Bayesian switching linear dynamic model. The proposed formulation leads to a tractable learning procedure and to an efficient gaze-and-VFOA tracking algorithm. The method is tested and benchmarked using a publicly available dataset that contains typical multi-party human-robot interaction scenarios, and that was recorded with both a motion capture system, and with a camera mounted onto a robot head. Index Terms-Visual focus of attention, eye gaze, head pose, dynamic Bayesian model, switching Kalman filter, multi-party dialog, human-robot interaction.
REF proposed a temporal graphical model to jointly track people's gaze and VFOA.
Tracking Gaze and Visual Focus of Attention of People Involved in Social Interaction
IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(11), 2711 - 2724, 2018
Computer Science Medicine