Development Of An Eye-Blink Detection System To Monitor Drowsiness Of Automobile Drivers
Keywords:
Drowsiness, blink detection, blinks frequency, drowsiness detection, fatigueAbstract
This paper presents a study that employed human-computer interaction (HCI) in the development of an improved driver’s drowsiness detection system (DDS) for monitoring the drowsiness of car drivers. The basic process used motion analysis technique for eye detection, which involves the analysis of the involuntary blinks of a user of the system. After the system had been initialized, the eye was tracked using the square difference matching method. The major parameter subsequently used to detect drowsiness was the frequency of blinks, such that an alarm is triggered when it gets to a critical level. The results demonstrated that a low cost webcam, with a capture rate of 30 frames/s and resolution of 320 x 240, was used to achieve a blink accuracy of 94.8%, missed blink error of 2.4%, false positive error of 3%. Also, an eye tracking accuracy of 72% at a distance of about 30cm was obtained. An improvement on the accuracy and reliability of this system over existing ones was achieved.
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