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Design and Implementation of an Image-Based Driver Attention Warning System on Low-Cost DSP Platform
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 12, 2010 by SAE International in United States
Annotation ability available
This paper proposes an image-based vehicle safety system, which has the ability to monitor the driver's status during their driving in all environment conditions. The image process technique, used to detect the driver's face by AdaBoost-based algorithm, is suitable to be applied under the internal environments of cabin. By analyzing the position of driver's face, once the driver's face disappeared in a reasonable area, the system will regard it as inattentive driving. When the acts of inattentive driving occurred, the system will generate warning signals through a buzzer or other devices to draw the driver's attention to notice on his driving. This system has been implemented on low-cost DSP platform and it can be installed efficiently on varied kind of vehicles. Under day and night driving conditions with series of specific viewing angles (from ahead, left/right-turn and turn-back views, which are corresponded to the angles in 0°~±90° and 180°), the averaged correct warning rate is 92.1%. In addition, under the circumstance of attentive driving, the correct warning rate is 96.08%.
CitationLiao, Y., Weng, M., and Chen, C., "Design and Implementation of an Image-Based Driver Attention Warning System on Low-Cost DSP Platform," SAE Technical Paper 2010-01-0713, 2010, https://doi.org/10.4271/2010-01-0713.
- The 100-Car Naturalistic Driving Study Phase II - Results of the 100-Car Field Experiment National Highway Traffic Safety Administration (NHTSA) April 2006
- The Impact of Driver Inattention on Near-Crash/Crash Risk: An Analysis of 100-Car Naturalistic Driving Study Data National Highway Traffic Safety Administration (NHTSA) April 2006
- Grace “Drowsy driver monitor and warning system,” Proc. Int. Driving Symp. Human Factors Driver Assessment, Training and Vehicle Design Aspen, CO Aug. 2001 64 69
- Seeing Machines 2004 Aug. Facelab Transport http://www.seeingmachines.com/transport.htm
- Application Note 1118: Compliance of Infrared Communication Products to IEC 825-1 and CENELEC EN 60825-1 Palo Alto, CA Agilent Technologies, Inc. 1999
- Viola, P. Jones M. “Robust Real-time Object Detection,” Proceedings of IEEE Workshop on Statistical and Theories of Computer Vision 2001
- Jensen, K. Anastassiou D. “Subpixel edge localization and the interpolation of still images,” IEEE Trans. On Image Processing 4 285 295 Mar. 1995
- Sonka, M. Hlavac V. Boyle R. Image Porcessing, Analysis, and Machine Vision PWS Pubishing 67 68 1999
- Phillips, P. J. Moon H. Rauss P. J. Rizvi S. “The FERET evaluation methodology for face recognition algorithms” IEEE Transactions on Pattern Analysis and Machine Intelligence 22 10 October 2000
- http://www.cam-orl.co.uk/facedatabase.html AT&T Laboratories Cambridge
- Turk, M. Pentland A. “Eigenfaces for recognition,” J. Cognitive Neuroscience 3 1 71 86 1991
- Graham, D. B. Allinson N. M. “Characterizing virtual eigensignatures for general purpose face recognition” Face Recognition: From Theory to Applications, NATO ASI Series F, Computer and Systems Sciences Wechsler H. Phillips P. J. Bruce V. Fogelman-Soulie F. Huang T. S. 163 446 456 1998