Distraction Detection through Facial Attributes of Transport Network Vehicle Service Drivers

Factors related to distraction have been one of the major causes of accidents related to driving. This paper presents a model that detects distraction cues of a Grab driver that mainly focuses on visual distraction and continuous communication indicated by mouth movements. The data gathered was in the form of a video footage obtained from each transit recorded. Each video was processed frame by frame to extract necessary features for detecting distraction cues through OpenFace. Binary classification was used to assess whether the driver is distracted or not. To determine the predictive power of the model, K-fold cross validation method was used. Since Grab has been widely used in the Philippines, the findings would allow for interesting knowledge regarding the behavior that these drivers exhibited in varying situations with regard to distraction.

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