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automated driver’s fatigue detection system

automated driver’s fatigue detection system The purpose of this project is to design an automated driver’s fatigue detection system which uses both image processing and machine learning techniques to detect driver’s fatigue by finding micro-sleep patterns. Contrary to conventional methods, images are acquired by placing camera on the left side of the driver rather than from the front. However, that renders one of the eyes mostly invisible to the camera. Thus, classification to find whether the eye is closed or open may be done on just one eye. Depending on the eye state, if found closed in consecutive frames it means driver is drowsy and an alarm is generated to alert the driver. Note:

  • 40% of the work is already done (one video, face & eye detection, code & template for result’s file). Eye detection results needs to be improved and classification by SVM + SIFT are remaining.
  • I can share all the material so you can improve my code or you can make it from scratch.
  • Average accuracy by 2 classification methods must be 95%.
  • Code must have proper comments and should be self-explanatory & easy to understand.
  • Timeline is 7 to 10 days.
  Milestones: 5 Videos and classification through SVM with average accuracy of 95%                                              60% of total payment Classification through SIFT with average accuracy of 95%]]>

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