Real-Time Distraction Detection Based on Driver’s Visual Features

Abstract

Driver’s distraction has been listed as the

leading contributing factor to traffic accidents for the past

decades. This paper focuses on developing an approach to

detect distraction real time by analyzing driver’s visual feature

from the face region. The proposed approach uses visual

features such as movement of eye and head to extract critical

information to detect driver attention states and to classify it as

either attentive or distracted. Deviation of eye center and head

from their standard position for a period of time is considered

to be useful cues for detecting lack of attention in this

approach. At first face detection is performed after which

region of interest (ROI) - eye and head region, are extracted

using facial landmarks and lastly, head and eye movements are

detected to classify attention state. To evaluate the system

performance, we conducted an experiment in a real driving

environment with subjects having different characteristics.

Our system achieved on average 92% accuracy in detecting

attention state for all tested scenarios.