Physiological Measure of Drowsiness Using Image Processing Technique
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2016, Vol 4, Issue 7
Abstract
Every year, most of the accidents occur due to drowsiness of drivers which cause severe casualties. Drowsiness reduces the perception level and decision making capability of the driver which negatively affect the ability of the driver to control the vehicle. Proper attention must be paid towards reducing the accidents that occur due to driver drowsiness. Monitoring driver behavior is one of the best ways to prevent fatal accidents. Proposed model detects drowsiness condition of driver by analyzing his eye blink rate and yawning state. The shape and appearance feature to detect eye blink and yawning state is extracted using Histogram of Oriented Gradient (HOG) feature from detected face. Linear Support Vector Machine (SVM) is employed for classification in two stages. In the first stage, classifier is employed for eye blink detection and in the second stage the classifier is employed for yawning detection. The proposed work is implemented using MATLAB 2015a and detection rate was found to be 93%. The use of HOG feature for drowsiness detection makes the system robust to age, gender, small pose variations and real time implementation is made much easier.
Authors and Affiliations
Naveen Kumar H N, Dr. Jagadeesha S
Keywords
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