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Driver Distraction Detection

EasyChair Preprint no. 5469

4 pagesDate: May 5, 2021


Number of road accidents is continuously increasing in the last few years worldwide. As per the survey of the National Highway Traffic Safety Administrator, nearly one in five motor vehicle crashes are caused by distracted drivers.The proposed approach uses visual features like movement of eye and head to extract critical information to detect driver attention states and to classify it as either attentive or distracted.Initially face detection is performed after which region of interest (ROI) - eye, mouth and head region, are extracted using facial landmarks and lastly, head and eye movements are detected to classify attention state.

Keyphrases: detection, Distraction, Drowsiness

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Indu Dokare and Akshay Lalwani and Pooja Patil and Rahul Ramrakhiani},
  title = {Driver Distraction Detection},
  howpublished = {EasyChair Preprint no. 5469},

  year = {EasyChair, 2021}}
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