Abstract
Driver’s drowsiness is one of the leading contributing factors to the increasing accidents statistics in Malaysia. Therefore, the design and development of driver drowsiness detection based on image processing using Raspberry Pi camera module sensor interfacing with Raspberry Pi 3 board are proposed in this paper. To achieve the aim of the research, the Haar Cascade Classifier algorithm is implemented for eyes and face detection whereas for eyes blink (open and close) detection, the Eye Aspect Ratio (EAR) algorithm is employed. From several experiments conducted on six recruited subjects, the findings revealed that the accuracy of Haar Cascade classifier to detect the eyes and faces was subjected to correct sitting position (head must facing to the camera) as well as the eyes must not be covered with glasses or shades. Meanwhile, the range of average EAR value detected by the system was between 0.141 (eyes closed) and 0.339 (eyes opened). In conclusion, the image processing-based Haar Cascade and EAR algorithms utilized on Raspberry Pi platform have been successfully executed. For future improvement, the current board can be replaced with Raspberry Pi Touch Screen to minimize the hardware setup and the physiological based analysis using alcohol and heart rate sensors can be added.
Highlights
The accident statistics as published in the website of Malaysia Road Safety Department showed an increment from 476,196 cases (2014) to 521,466 cases (2016)
The driver drowsiness has been one of the main reasons of road accidents in Malaysia and it can lead to physical injuries or worst; death
The process stage involves the software of Raspberry Pi B using Python with Open Source Computer Vision Library (Open CV) for image processing execution using Haar Cascade and Eye Aspect Ratio algorithms
Summary
The types of feedback to the driver in respond to the dangerous driving performance metrics were not mentioned as well In this project, the design and development of eyes and face recognition as well as eye blinks detection based-behavioural measure for driver drowsiness detection are being proposed using two main components only; Raspberry Pi 3 and Raspberry Pi 8 Mega Pixel camera sensor. The objectives of this research are to implement the Haar Cascade Classifier algorithm for face and eyes recognition, to employ the Eye Aspect Ratio algorithm for detecting eye blinks that indicate the drowsiness and to evaluate both algorithms on various situations using recruited subjects. The prototype, which consists of Raspberry Pi and Pi Camera is casing together and can be located inside the car
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