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Section: New Results
Estimation of Eye Gaze and of Visual Focus of Attention
We address the problem of estimating the visual focus of attention (VFOA), e.g. who is looking at whom? This is of particular interest in human-robot interactive scenarios, e.g. when the task requires to identify targets of interest and to track them over time. We make the following contributions. We propose a Bayesian temporal model that links VFOA to eye-gaze direction and to head orientation. Model inference is cast into a switching Kalman filter formulation, which makes it tractable. The model parameters are estimated via training based on manual annotations. The method is tested and benchmarked using a publicly available dataset. We show that both eye-gaze and VFOA of several persons can be reliably and simultaneously estimated and tracked over time from observed head poses as well as from people and object locations [40].
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