Drowsiness Detection using OpenCV Project


The new way of security system which will be discussed in this project is based on machine learning and artificial intelligence. Passenger security is the main concern of the vehicle’s designers where most accidents are caused due to drowsiness and fatigued driving in order to provide better security for saving the lives of passengers Airbags are designed but this method is useful after an accident accord.

But the main problem is still seeing many accidents happening and many of them are losing their lives. In this project we are using the OpenCV library for image processing and giving input as user live video and training data to detect if the person in the video is closing their eyes or showing any symptoms of drowsiness and fatigue then the application will verify with trained data and detect drowsiness and raise an alarm which will alert the driver.

Existing system:

There are various methods like detecting objects which are near a vehicle and front and rear cameras for detecting vehicles approaching near to vehicle and airbag systems that can save lives after an accident is accorded.


Most of the existing systems use external factors and inform the user about the problem and save users after an accident is an accord but from research, most of the accidents are due to faults in users like drowsiness and sleeping while driving.

Proposed system:

To deal with this problem and provide an effective system a drowsiness detection system can be developed which can be placed inside any vehicle it will take live video of the driver as input and compare it with training data and if the driver is showing any symptoms of drowsiness system will automatically detect and raise an alarm which will alert the driver and other passengers.


This method will detect a problem before any problem accord and inform the driver and other passengers by raising an alarm.

In this OpenCV-based machine learning techniques are used for the automatic detection of drowsiness.


  • Operating system: Windows 7.
  • Coding Language: python
  • Tool: anaconda, visual studio code
  • Libraries: OpenCV

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