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Robust and continuous estimation of driver gaze zone by dynamic analysis of multiple face videos

Analysis of driver's head behavior is an integral part of driver monitoring system. Driver's coarse gaze direction or gaze zone is a very important cue in understanding driver-state. Many existing gaze zone estimators are, however, limited to single camera perspectives, which are vulnerable to occlusions of facial features from spatially large head movements away from the frontal pose. Non-frontal glances away from the driving direction, though, are of special interest as interesting events, critical to driver safety, occur during those times. In this paper, we present a distributed camera framework for gaze zone estimation using head pose dynamics to operate robustly and continuously even during large head movements. For experimental evaluations, we collected a dataset from naturalistic on-road driving in urban streets and freeways. A human expert provided the gaze zone ground truth using all vision information including eyes and surround context. Our emphasis is to understand the efficacy of the head pose dynamic information in predicting eye-gaze-based zone ground truth. We conducted several experiments in designing the dynamic features and compared the performance against static head pose based approach. Analyses show that dynamic information significantly improves the results.