Driver Drowsiness Detection System Using Image Processing Test phase, srs, design phase and source code final deliverable
Category
Web Application/Artificial Intelligence/Image Processing
Abstract / Introduction
Driver drowsiness and fatigue are significant causes of road accidents. The number of fatalities due to such accidents is increasing worldwide each year. A countless number of people drive on the highway day and night. Taxi drivers, bus drivers, truck drivers and people traveling longdistance suffer from lack of sleep. Due to which it becomes very dangerous to drive when feeling sleepy.
Drowsiness detection is a safety technology that can prevent accidents that are caused by drivers who fell asleep while driving. It will reduce the number of accidents caused by driver fatigue and thus increase transportation safety; this system deals with automatic driver drowsiness detection based on visual information and Artificial Intelligence.
The proposed OpenCV algorithms effectively find and help to normalize human faces while causing the majority of accidents related to vehicle crashes. Several faces and body gestures, including tiredness in the eyes and yawning, are regarded as signs of drowsiness and fatigue in drivers. These characteristics indicate that the driver’s condition is poor.
Functional Requirements:
- Admin and the users can sign up in the system with required data.
- Admin and the users can sign in to the system with the username and password.
- The system shall apply all the validations to the data provided at the time of registration and login.
- The admin can view the list of all the users.
- The admin can view user’s logs.
- The user has to register their account and log in using a username and password.
- The system will detect eye closure.
- EAR (Eye Aspect Ratio) computes the ratio of distances between the horizontal and vertical eye landmarks which is required for detection of drowsiness.
- The system will detect yawning actions in real-time using the OpenCV.
10.For the purpose of yawn detection, a YAWN value is calculated using the distance between the lower lip and the upper lip, and the distance will be compared against a threshold value.
11.It will draw a red rectangle if there is any detection.
12.System will ring an alarm within seconds if there is any detection.
Tools:
- Python
- Sublime text Editor
- XAMP Server
Kindly contact to me on Skype or email me for the dataset.
Following are the research papers that you can read for the better understanding. https://www.ijert.org/driver-drowsiness-detection
- https://www.ijraset.com/research-paper/driver-drowsiness-detection-using-ml
- https://ieeexplore.ieee.org/document/6602353
Note: Kindly read the proposal carefully and decide if you have completely understood the project requirements before selecting the project. Please feel free to discuss any project- related questions before selecting it.
Supervisor:
Name: Fizzah
