Observations and Conclusions

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Observations 


 
Graphical Illustration of the differences in accuracy with additions to dataset 
 
  • The below figure shows the ratio of number of correctly classified images to total number of images for a test set of 30 images. This is displayed in the form of a column chart for different training data sets. On adding new coloured eye images, the accuracy of the model actually drops. This is because the classifier gets confused with more of a variety of pictures. There’s more of a conflict between images in the open set and images in the closed set. This reduces the accuracy. The algorithm to detect the lip based on colour fails as there isn’t much of a gradient colour difference. The classification of yawn using SVM classifier isn’t very accurate. With respect to the blink model, the model would perform better if the mouth area was already localised and the classifier worked on the cropped image. This would reduce dependencies from the rest of the image.
Results with all eye types of image added dataset
  • The algorithm to detect the lip based on colour fails if there isn’t much of a gradient colour difference. The classification of yawn using SVM classifier isn’t very accurate. With respect to the blink model, the model would perform better if the mouth area was already localised and the classifier worked on the cropped image. This would reduce dependencies from the rest of the image.


Conclusions 


  • The project has presented various methods for drowsy driver detection and has compared the various techniques. 
  • The method of finding differences between frames gave us the best results for detecting a change. It works well for all test cases. 
  • Although, the classification model measures how long the driver's eye has been closed giving us better results in reality.
  • With respect to the various yawn models that we have employed, the classifier model works best. 


Future Enhancements: 


  • Incorporation of the drivers head drops / unusual body movements. 
  • Different colour of lips Better Yawn detection 
  • Take into account steering patterns and lane monitoring.

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