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Testing Our Models

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Testing our Blink Detection models We test the blink model (The differences method) and eye model ( Classifier) with multiple test cases. With respect to the blink model, we test our model with images of: • Different eye colours • Different sizes The first method of finding differences works well with all the above cases as differences are noticed irrespective of the above conditions. The threshold values for the number of white pixels have to be changed for different types. Colored eye bw binary image.  In the second method of training a classifier, we initially have a test data set of only images of a particular eye colour and size.  Results with only one type of image in dataset This model gives us great results for different test cases of similar size and colour. The confusion matrix for this along with average accuracy for each run is displayed. Each run picks a random set of training and test data as mentioned previously. This reduces bias ...
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Training a Classifier

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Using an SVM Classifier to detect sleepiness: This algorithm works primarily on the principle that if the person has kept his eyes closed for too long the driver is detected to be sleepy. After extracting each frame from a video, we manually classify each image as open or closed eye. We then separate our dataset of images into training and test data - with 30 percent being training-data and the rest 60 percent being test-data. We do this split randomly. This makes our training and test data different each time we run the program. Using SVM Classifier, we generate a model based on a bag of features. We find the accuracy of the model using confusion matrices. The next step is to feed the video and to get labels as open or closed  for each new frame. Whether the eyes are open or closed in each frame. We keep track of how long the eyes have been closed continuously by resetting a count variable (that counts the elapsed time ) each time the eye opens. If the count is beyond a certain...
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Introduction

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Recently, driver drowsiness has been one of the highest reasons for road accidents. Around 20% of all road accidents are fatigue related and 50% on some roads. Driver drowsiness detection aims at preventing these accidents caused by the driver getting drowsy by identifying so and setting off an alarm to warn the driver. There are many ways to detect drowsiness of the driver : 1. Vehicle Based : Steering pattern monitoring, Vehicle position in lane monitoring 2. Behavioural Based : Driver eye/face monitoring 3. Physiological Based : ECG, EMG etc We aim at studying the Behavioural based approach. References : 1. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.458.7214&rep=rep1&type=pdf 2. https://ntl.bts.gov/lib/jpodocs/repts_te/7068.pdf 3. https://pdfs.semanticscholar.org/71bc/acba6bbd44ef330432ce1603c8874ca35d03.pdf 4. https://www.ri.cmu.edu/pub_files/pub2/grace_richard_2001_1/grace_richard_2001_1.pdf
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