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Enhancing Video Signal Quality and Algorithm Advances in PAF Studies

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Explore recent advancements in PAF studies including new data from driver studies, methods to improve video quality and learning algorithms, and findings from a study with undergraduates under varied conditions. Future directions involve refining algorithms, replicating studies, testing individual differences, and more.

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Enhancing Video Signal Quality and Algorithm Advances in PAF Studies

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  1. PAF Update11/2/05

  2. New Results since last time • New addition: Manos Pontikakis • Further refined data from “dark drowsy driver study” • Parsing out Usable vs. Nonusable video data • Objective techniques to improve the video signal itself • New types of learning algorithms • New types of inputs to the algorithms • Differently timed PAFs • New study, 20 Stanford Undergraduates • Brightly lit conditions • Much more usable data • Very reliably getting about 8 percentage points above chance with PAF • Trying different temporal windows (10-20 seconds working best)

  3. Future Directions • Run more subjects under different conditions • Lighting, camera type, driving course details, etc. • Refine algorithm • Replicate • Test for individual differences • Test in “real time” • Different Subject Groups? • Focus more on within-driver • Bring back the same people? • Test awareness of PAF • Less accidents? • Subjective of videos • Other person-attribute driving behaviors for paf

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