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OMRON/Stanford PAF Workshop

OMRON/Stanford PAF Workshop. 8/2/05. Overview. Phase I results (Drowsiness Study) Phase I results (Face Tracking and algorithmic Identification of Drowsiness and PAF) Information about facial points (amount of movement, predictability of points to help OKAO Development) Phase II and beyond.

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OMRON/Stanford PAF Workshop

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  1. OMRON/Stanford PAF Workshop 8/2/05

  2. Overview • Phase I results (Drowsiness Study) • Phase I results (Face Tracking and algorithmic Identification of Drowsiness and PAF) • Information about facial points (amount of movement, predictability of points to help OKAO Development) • Phase II and beyond

  3. Drowsiness Study • 80 subjects (40 drowsy by Epworth Scale, 40 normal • 35 minutes of driving behavior from each • 2 Cameras • Measured poor behavior: • Swerves • Accidents • Stop Sign Violations

  4. Drowsiness study: Driving Behavior

  5. SVMs: Linear vs. Nonlinear

  6. SVMs: Training versus Testing • Training set: 70 percent of the data • Testing set is 30 percent • Reduce amount of data • Different subjects in training and testing • Training takes 60 hours with full data set, 2 hours with reduced set (about 145,000 iterations of trying to fit the data) • Testing takes 15 minutes

  7. PAF Classifier • SVM inputs are samples taken at 1/15th of a second • A time input sample includes 53 points (x, y of 22 points and 9 scalars due to orientation, and scale and aspect ration of eyes and mouth) • Output training is a 1 or a zero (1 is accident, swerve out of the road, or running a red light) • Output training is a time sample N seconds after the input is taken (n = 1,3, or 5) • SVMs can be linear or nonlinear • Across or Within Drivers

  8. PAF Classifier—Across Drivers Drive Performance -1 = good (50 % of the reduced data) 1 = bad (all of the data) P1x…P1y…P2X…P2y…P3x…P3y…………….P53

  9. PAF • Best classifier for Across Drivers around 70 percent, but most are just above fifty percent • Best classifier for Within Drivers—about 80 percent success rates (train on random 2/3rds of events), best for 3 second PA -But this is preliminary, and the effect may go down when we adjust for how few data points we have for each driver in terms of bad behavior!

  10. PAF Classifier—Within Drivers Drive Performance -1 = good (from 2/3rds of subject X) 1 = bad (from 2/3rds of subject X) P1x…P1y…P2X…P2y…P3x…P3y…………….PN

  11. Drowsy Driver Classifier Driver Type -1 = normal (50 % of drivers) 1 = drowsy (50 % of drivers) P1x…P1y…P2X…P2y…P3x…P3y…………….PN

  12. SVM: Drowsy Driver detection

  13. Omron and OKAO: Amount of Movement (7.6,9.9) (7.1,8.8) (5.1,3.0) (5.1,4.7) (7.5,9.1) (3.0,3.1) (6.0,6.9) (7.0,6.2) (4.2,3.5) (4.3,4.8) (3.3,4.4) (4.9,5.7) (5.4,4.5) (6.6,6.9) (8.7,5.0) (3.9,2.2) (2.6,1.4) (7.5,9.8) (5.7,6.2) (5.8,6.1) (6.0,7.6) (7.2,5.0)

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