Drowsy Driver Detection

We implemented the first four steps of the flowchart: We used the VideoReader and read functions in matlab to read a video and extract every frame of the video. We used the inbuilt functions in Matlab for face detection based on the Viola Jones algorithm -  https://in.mathworks.com/help/vision/ref/vision.cascadeobjectdetector-class.html -  https://en.wikipedia.org/wiki/Viola%E2%80%93Jones_object_detection_framework CODE: clc; blink = VideoReader('blinky2.mp4'); for img = 290:291;      b = read(blink, img);     FDetect = vision.CascadeObjectDetector;     I = b;     BoundBox = step(FDetect,I);     figure,     imshow(I); hold on     for i = 1:size(BoundBox,1)         rectangle('Position',BoundBox(i,:),'LineWidth',5,'LineStyle','-','EdgeColor','r');     end     for i = 1:size(BoundBox,1)         J= imcrop(I,BoundBox(i,:));         hold off;         imshow(J);hold on     end end

We intend to follow these steps :

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