How to Build a Drowsy Driver Detection System Using Matlab Code
Driving while drowsy is a major cause of road accidents, especially for long-distance drivers. According to the National Highway Traffic Safety Administration, drowsy driving was responsible for 795 deaths and 50,000 injuries in 2017 in the United States alone. To prevent such tragedies, it is important to develop a system that can monitor the driver's alertness and warn them if they are falling asleep.
Drowsy Driver Detection System Using Matlab Code
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One way to achieve this is by using computer vision techniques to analyze the driver's face and eye movements. In this article, we will show you how to build a drowsy driver detection system using Matlab code. We will use the drowsiness-detection GitHub topic as a source of inspiration and reference. We will also explain the main steps and challenges involved in this project.
Step 1: Face and Eye Detection
The first step is to detect the driver's face and eyes in a real-time video stream from a camera mounted on the dashboard or windshield. This can be done using the vision.CascadeObjectDetector system object in Matlab, which implements the Viola-Jones algorithm. This algorithm uses a cascade of simple features to quickly reject non-face regions and locate potential face regions in an image. The same object can be used to detect eyes within the face region by specifying a different XML file that contains the trained classifier for eyes.
The following code snippet shows how to create and use the vision.CascadeObjectDetector object for face and eye detection:
```matlab
% Create a cascade object detector for face detection
faceDetector = vision.CascadeObjectDetector('FrontalFaceCART');
% Create a cascade object detector for eye detection
eyeDetector = vision.CascadeObjectDetector('EyePairBig');
% Read a video frame from a webcam
videoFrame = snapshot(webcam);
% Detect faces in the video frame
bboxFace = faceDetector(videoFrame);
% Draw a rectangle around the detected face
videoFrame = insertShape(videoFrame,'Rectangle',bboxFace);
% Detect eyes within the face region
bboxEye = eyeDetector(videoFrame(bboxFace(2):bboxFace(2)+bboxFace(4),bboxFace(1):bboxFace(1)+bboxFace(3),:));
% Adjust the eye bounding box coordinates relative to the face region
bboxEye(:,1) = bboxEye(:,1) + bboxFace(1);
bboxEye(:,2) = bboxEye(:,2) + bboxFace(2);
% Draw a rectangle around the detected eyes
videoFrame = insertShape(videoFrame,'Rectangle',bboxEye);
% Display the video frame with face and eye detection
imshow(videoFrame);
```
Step 2: Eye Tracking and Blinking Detection
The next step is to track the driver's eyes and detect if they are blinking or not. This can be done using the vision.PointTracker system object in Matlab, which implements the Kanade-Lucas-Tomasi (KLT) algorithm. This algorithm tracks a set of feature points across multiple video frames using optical flow. The feature points can be selected manually or automatically using the detectMinEigenFeatures function.
The following code snippet shows how to create and use the vision.PointTracker object for eye tracking:
```matlab
% Create a point tracker object for eye tracking
pointTracker = vision.PointTracker('MaxBidirectionalError',2);
% Initialize the tracker with the initial eye locations
points = detectMinEigen 29c81ba772
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