Drowsy Detection On Eye Blink Duration Using Algorithm
Mandeep Singh1, Gagandeep Kaur2
Student, M.tech Electronics and Communication Section, YCOE, Talwandi Sabo, Guru Kashi Punjabi University Campus, Patiala 2
Asst Prof., Electronics and Communication Section, YCOE, Talwandi Sabo, Guru Kashi Punjabi University Campus, Patiala 1
Abstract— this paper presents an automatic drowsy driver monitoring and accident prevention system that is based on monitoring the changes in the eye blink duration. Our proposed method detects the drowsiness in eyes using the proposed mean sift algorithm. Our new method detects eye blinks via a standard webcam in real-time YUY2_640x480 resolution. Experimental results in the eyeblink database showed that the proposed system detects eye blinks with 99.4% accuracy with a 1% false positive rate. Keywords - Eye blinks detection, eye symmetry, and drowsiness detection.
II. FLOW CHART Interfacing a camera through MATLAB
I. INTRODUCTION In physiology fatigue is to perform reasonable and necessary physical or mental activity. When the metabolic reserves of the body are exhausted and the waste products increased, as for example after prolonged exertion, the body finds it difficult to continue its function and activity. The ever increasing numbers of traffic accidents all over the world are due to the drowsiness of driver. Drowsy driving is a factor in one in every six road accidents and one in three heavy vehicle accidents. The annual financial toll is estimated to be at least $2 billion in health and insurance costs in Australia and $56 billion in the U.S. A 2009 survey by the National Transport Commission found that at least 45 per cent of heavy vehicle drivers were impaired by fatigue during their last shift. It found 52 per cent of major crash insurance claims were fatigue related. The survey also revealed 50 per cent of all long distance truck drivers had nodded off while driving more than once. According to the National Sleep Foundation’s 2010 Sleep in America poll, 60% of adult drivers have driven a vehicle while feeling drowsy in the past year, and more than one-third have actually fallen asleep at the wheel.
Start the camera in back round by trigger method
Cut the eye area from real Image
Make the origin to eyebrows and take a box which covers the eyes
Calculate the percentage of eyes opening
If eye opening percentage decreases from desired value
International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, Volume 2, Issue 4, April 2012)
The focus of this paper is on last category of alertness monitoring technologies. Those technologies monitor in real time. In this paper take only the eye parameters in real time image taken by trigger method because it takes less time than the preview method to check the drowsiness level of the driver. To become it practical and useful we use the warning system, this is a real world driving environment. The objective of this paper is to measure the current activity of the eyes of the driver which is visualized by the camera and we can check the drowsiness of the driver. In this we give the value of the eye closure percentage value and the time for which alarm blown, this is changed according to every person, it set in the program. When the alarm blown it continuously checks the eye pattern of eye closure if percentage increases then the alarm goes off. This is done in the while loop because it goes continuous, it do not break until we do not want. III. IMAGE PROCESSING This approach analyzes the images captured by camera to detect physical changes of drivers, such as eyelid movement, using MATLAB Software. Using image processing technique to measure the...