Comparing two video-based techniques for driver fatigue detection: classification versus optical flow approach

  • Authors:

  • Rajinda Senaratne;Budi Jap;Sara Lal;Arthur Hsu;Saman Halgamuge;Peter Fischer

  • Affiliations:

  • The University of Melbourne, Department of Mechanical Engineering, Melbourne School of Engineering, 3010, Melbourne, VIC, Australia;University of Technology Sydney, Department of Medical and Molecular Biosciences, 2007, Sydney, NSW, Australia;University of Technology Sydney, Department of Medical and Molecular Biosciences, 2007, Sydney, NSW, Australia;The Walter and Eliza Hall Institute of Medical Research, Bioinformatics Division, 3050, Melbourne, VIC, Australia;The University of Melbourne, Department of Mechanical Engineering, Melbourne School of Engineering, 3010, Melbourne, VIC, Australia;Signal Network Technology Pty Ltd, Lane Cove, 1595, Sydney, NSW, Australia

  • Venue:
  • Machine Vision and Applications
  • Year:
  • 2011

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Abstract

Lack of concentration in a driver due to fatigue is a major cause of road accidents. This paper investigates approaches that can be used to develop a video-based system to automatically detect driver fatigue and warn the driver, in order to prevent accidents. Ocular cues such as percentage eye closure (PERCLOS) are considered strong fatigue indicators; thus, accurately locating and tracking the driver’s eyes is vital. Tests were carried out based on two approaches to track the eyes and estimate PERCLOS: (1) classification approach and (2) optical flow approach. In the first approach, the eyes are tracked by finding local regions, the state (open or closed) of the eyes in each image frame is estimated using a classifier, and thereby the PERCLOS is calculated. In the second approach, the movement of the upper eyelid is tracked using a newly proposed simple eye model, which captures image velocities based on optical flow, thereby the eye closures and openings are detected, and then the eye states are estimated to calculate PERCLOS. Experiments show that both approaches can detect fatigue with reasonable accuracy, and that the classification approach is more accurate. However, the classification approach requires a large amount of suitable training data. If such data are unavailable, then the optical flow approach would be more practical.