Computer Vision-based Eye Detection and Warning System for Driver Fatigue
Date Issued
2005
Date
2005
Author(s)
Chen, Yi-Ru
DOI
en-US
Abstract
This study developed computer vision-based eye detection and warning system. The purpose of this system is to perform detection of driver fatigue. By mounting a small infrared camera on the dashboard, a driver’s eyelid movements are recorded. While this driver is tired and no longer in condition to drive, a warning signal would be issued, and at urgent time, an emergency call would deliver to Telematics Call Center automatically where agents could offer a real-time help to link the driver by voice-activated cellular phone.
Eye Detection Module contains three major parts: face detection, eye detection, and eye tracking. Face detection algorithms are developed to reduce searching areas. These methods are based on both ellipse-template matching in daytime and dynamic threshold selection in nighttime. After determining the face, an accumulative different image method is used to extract eye candidates in primary step. Further, an edge detector is used to find the most possible eye targets to separate other objects such as eyebrows. Finally, to make the module more robust, a α-β filter is used to track eye targets in subsequence frame. This eye detection module is used to calculate the breadth of eye open to determine vigilance levels of a driver.
To monitor a driver fatigue, both Percentage of Eye Closure over Time (PERCLOS) and Average Eye Closure Speed (AECS) are calculated. Because eyelid movement characters are distinct from people, a Backpropagation Neural Network is applied to learn and train the adaptive vigilance levels by different drivers. Combing vigilance levels and vehicle speeds, a warning threshold is determined by Fuzzy Membership Functions, which can prevent unnecessary warning signals. For example, although a driver is very tired, the alert wouldn’t work if the car moves slowly.
There are total 4 test cases in the experimental results. Only one case failed because this driver wearing glasses which cause optical-flow to reduce eye detection accuracy. But in this failed case, the probability of success is still around 84.8 %. The probabilities of success are all above 97.8 % in the other cases. It shows this system is feasible to most cases.
Subjects
駕駛疲勞
眼睛偵測
倒傳遞類神經網路
模糊隸屬函數
Telematics客服中心
Driver Fatigue
Eye Detection
Backpropagation Neural Network
Fuzzy Membership Function
Telematics Call Center
Type
thesis
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