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Title: Driver drowsiness detection based on eye blink
Author: Bandara, Indrachapa Buwaneka
ISNI:       0000 0004 2675 0838
Awarding Body: Brunel University
Current Institution: Bucks New University
Date of Award: 2009
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Accidents caused by drivers’ drowsiness behind the steering wheel have a high fatality rate because of the discernible decline in the driver’s abilities of perception, recognition, and vehicle control abilities while sleepy. Preventing such accidents caused by drowsiness is highly desirable but requires techniques for continuously detecting, estimating, and predicting the level of alertness of drivers and delivering effective feedback to maintain maximum performance. The main objective of this research study is to develop a reliable metric and system for the detection of driver impairment due to drowsiness. More specifically, the goal of the research is to develop the best possible metric for detection of drowsiness, based on measures that can be detected during driving. This thesis describes the new studies that have been performed to develop, validate, and refine such a metric. A computer vision system is used to monitor the driver’s physiological eye blink behaviour. The novel application of green LED illumination overcame one of the major difficulties of the eye sclera segmentation problem due to illumination changes. Experimentation in a driving simulator revealed various visual cues, typically characterizing the level of alertness of the driver, and these cues were combined to infer the drowsiness level of the driver. Analysis of the data revealed that eye blink duration and eye blink frequency were important parameters in detecting drowsiness. From these measured parameters, a continuous measure of drowsiness, the New Drowsiness Scale (NDS), is derived. The NDS ranges from one to ten, where a decrease in NDS corresponds to an increase in drowsiness. Based upon previous research into the effects of drowsiness on driving performance, measures relating to the lateral placement of the vehicle within the lane are of particular interest in this study. Standard deviations of average deviations were measured continuously throughout the study. The NDS scale, based upon the gradient of the linear regression of standard deviation of average blink frequency and duration, is demonstrated as a reliable method for identifying the development of drowsiness in drivers. Deterioration of driver performance (reflected by increasingly severe lane deviation) is correlated with a decreasing NDS score. The final experimental results show the validity of the proposed model for driver drowsiness detection.
Supervisor: Not available Sponsor: Buckinghamshire New University
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available