realtime driver drowsiness detection system for driving safety. Based on computer vision techniques , the driver`s face is located from a color video captured in a car. Then, face detection is employed to locate the regions of the driver`s eyes, which are used as the templates for eye tracking in subsequent frames. Finally, the tracked eye`s images are used for drowsiness detection in order to generate warning alarms. The proposed approach has three phases: Face, Eye detection and drowsiness detection. The role of image processing is to recognize the driver's face and extract the image of the driver's eyes to detect drowsiness. The hair face detection algorithm takes the captured image frame as input and then the detected face as output. Then use CHT to track the eyes from the detected face. If the driver's eyes are closed for a predefined period of time, the driver's eyes are considered closed and an alarm is triggered to alert the driver. The proposed system was tested on a Raspberry Pi 3 Model B with 1GB of RAM using the Logitech HD Webcam C270. The experimental results seem very promising and promising. The system can achieve more than 15 frames per second for face and eye tracking, and in some test videos, the average eye position and tracking rate can reach 99.0%. Therefore, we can conclude that the proposed approach is a cheap and effective solution for real-time driver drowsiness detection..
suntech software solutions provides best switch to on using Raspberry Pi Pico project with complete description and project report, and complete on call guidance related to the project order now.
Component List
- raspberry pi
- Webcam
- Powersupply
- Jumpers and wires
- motor
- Vibration Belt
- buzzer
- Speaker
- LCD
Features
- Alerts the driver by detecting if he is drowsy, and vibrates the bend he have weared
