Driver-Gaze Zone Estimation Using Bayesian Filtering and Gaussian Processes

Journal article, 2016

In this paper, we propose a Bayesian filtering approach that uses information from camera-based driver monitoring systems and filtering techniques to find the probability that the driver is looking in different zones. In particular, the focus is on a set of zones directly related either to active driving or to visual distraction, such as the road, the mirrors, the infotainment display, or control buttons. For systems that do not provide direct observations of the gaze direction or as a complement to noisy gaze data, we propose to use probabilistic functions that describe the gaze direction as a function of head pose and eye closure. It is further shown how these functions can be estimated from data with know visual focus points using Gaussian processes. Evaluation on data from two driver monitoring systems shows a significant improvement compared with the gaze zone estimates based on unprocessed data.