Driver Drowsiness Detection using Raspberry Pi

Computer Engineering

The proposed system aims to lessen the number of accidents that occur due to drivers’ drowsiness and fatigue, which will in turn increase transportation safety. This is becoming a common reason for accidents in recent times. In this, the driver is continuously monitored through webcam. This model uses image processing techniques which mainly focuses on face and eyes of the driver. Raspberry-pi processor is used for image processing. Image processing techniques such as Haar3-cascade frontal face and Face landmark shake predictor are used for acquiring details of given eye object and further processing. This proposed system is used for Driver Road safety system. In this system webcam capture driver face image. 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. 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 face of the driver and then extracts the image of the eyes of the driver for detection of drowsiness.

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