Non-invasive Techniques for Driver Fatigue Monitoring

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Presentation transcript:

Non-invasive Techniques for Driver Fatigue Monitoring Qiang Ji Dept. of Electrical, Computer, and Systems Engineering Rensselaer Polytechnic Institute qji@ecse.rpi.edu http://www.ecse.rpi.edu/~qji Funded by AFOSR and Honda

Visual Behaviors Visual behaviors that typically reflect a person's level of fatigue include Eyelid movement Head movement Gaze Facial expressions

Real time plot eyelid and eye gaze parameters over time Real time plot eyelid and eye gaze parameters over time. AECS represents the average eye closure and opening speed; PERCLOS is the percentage of eye closure; PERSAC is the percentage of saccade eye movements over time.

Real time plot of face pose parameters (pan, tilt, and swing) and facial expression parameter (mouth) over time. Face pose tracking is to characterize head activity such as nodding and mouth movement is used to detect mouth movement such as yawning.

The Dynamic Bayesian Network fatigue model for modeling and detecting fatigue. It combines different visual fatigue parameters with contextual information (if available) to produce a composite fatigue score.

An overview of the fatigue monitor prototype An overview of the fatigue monitor prototype. The prototype system: upper left corner shows the image from the eye camera; upper right corner shows the image of face camera; bottom shows the real time plot of the fatigue curve over time.

Dr. Qiang Ji of Rensselaer Polytechnic Institute in Troy, N.Y., Stewart Cairns for The New York Times Dr. Qiang Ji of Rensselaer Polytechnic Institute in Troy, N.Y., demonstrates a driver fatigue monitor. Dr. Qiang Ji of Rensselaer Polytechnic Institute in Troy, N.Y., demonstrates a driver fatigue monitor. From the business section of the New York Times Aug. 26, 2003.