Abstract
This study presents a new method to track driver’s facial states, such as head pose and eye-blinking in the real-time basis. Since a driver in the natural driving condition moves his head in diverse ways and his face is often occluded by his hand or the wheel, it should be a great challenge for the standard face models. Among many, Active Appearance Model (AAM), and Active Shape Model (ASM) are two favored face models. We have extended Discriminative Bayesian ASM by incorporating the extreme pose cases, called it Pose Extended—Active Shape model (PE-ASM). Two face databases (DB) are used for the comparison purpose: one is the Boston University face DB and the other is our custom-made driving DB. Our evaluation indicates that PE-ASM outperforms ASM and AAM in terms of the face fitting against extreme poses. Using this model, we can estimate the driver’s head pose, as well as eye-blinking, by adding respective processes. Two HMMs are trained to model temporal behaviors of these two facial features, and consequently the system can make inference by enumerating these HMM states whether the driver is drowsy or not. Result suggests that it can be used as a driver drowsiness detector in the commercial car where the visual conditions are very diverse and often tough to deal with.
Highlights
According to National Highway Traffic Safety Administration (NHTSA), drowsy driving causes more than 100,000 crashes a year [1]
Results indicate that DB-Active Shape Model (ASM) outperforms Appearance Model (AAM) in terms of MAE as well as Root Mean Squared Error (RMSE), as shown s gt = in ground truth, s shape
The conventional driver drowsiness detection systems are vulnerable to extreme head rotation and occlusion
Summary
According to National Highway Traffic Safety Administration (NHTSA), drowsy driving causes more than 100,000 crashes a year [1]. The whole alignment process can be degraded by an error on any landmark drawn on the tracking face To handle such problem, the Constrained Local Model (CLM) has been proposed [8], where each shape landmark detects the local feature and carries out tracking independently. Any error occurring in a local feature detector has minimal impact to the overall performance since each landmark behaves independently Due to such reasons, ASM based on CLM shows robustness against occlusion. We have used a Markov chain framework whereby the driver’s eye-blinking and head nodding are separately modelled based upon their visual features, and the system makes a decision by combining those behavioral states of whether the driver is drowsy or not, according to a certain criteria. The shape parameters use a Maximum A Posteriori (MAP) update
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