Development of Low-Cost Real-Time Driver Drowsiness Detection System using Eye Centre Tracking and Dynamic Thresholding
Organizations: Department of Electronics and Communication Engineering, National Institute of Technology Karnataka, Surathkal, India - 575025
Abstract
One in every five vehicle accidents on the road today is caused simply due to driver fatigue. Fatigue or otherwise drowsiness, significantly reduces the concentration and vigilance of the driver thereby increasing the risk of inherent human error leading to injuries and fatalities. Hence, our primary motive being - to reduce road accidents using a non-intrusive image processing based alert system. In this regard, we have built a system that detects driver drowsiness by real time tracking and monitoring the pattern of the driver's eyes. The stand alone system consists of 3 interconnected components - a processor, a camera and an alarm. After initial facial detection, the eyes are located, extracted and continuously monitored to check whether they are open or closed on the basis of a pixel-by-pixel method. When the eyes are seen to be closed for a certain amount of time, drowsiness is said to be detected and an alarm is issued accordingly to alert the driver and hence, prevent a casualty.
Figures & tables
| Thresholding method | Difference in White Percentage |
|---|---|
| Simple Binary | 7% |
| Otsu’s Thresholding | 8% |
| Niblack’s Thresholding | 10% |
| Bernsten’s Thresholding | 11.5% |
| Mean Gaussian Binary | 13% |
| Adaptive Gaussian Binary | 15% |
| Component | Specifications | Cost |
|---|---|---|
| Raspberry Pi 3 Model B | ARM Cortex-A53 CPU | $32 |
| Logitech C270 Webcam | Upto 30 FPS at 640x480 | $15 |
| RPi 3 compatible Alarm System | 9012 triode driven buzzer | $1.8 |
Driver Monitoring
Driver monitoring uses in-cabin cameras and other sensors to detect driver states and support safer, more responsive vehicles.
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