[HTML][HTML] Using image processing in the proposed drowsiness detection system design
M Poursadeghiyan, A Mazloumi, GN Saraji… - Iranian journal of …, 2018 - ncbi.nlm.nih.gov
Iranian journal of public health, 2018•ncbi.nlm.nih.gov
Background: Drowsiness is one of the underlying causes of driving accidents, which
contribute, to many road fatalities annually. Although numerous methods have been
developed to detect the level of drowsiness, techniques based on image processing are
quicker and more accurate in comparison with the other methods. The aim of this study was
to use image-processing techniques to detect the levels of drowsiness in a driving simulator.
Methods: This study was conducted on five suburban drivers using a driving simulator based …
contribute, to many road fatalities annually. Although numerous methods have been
developed to detect the level of drowsiness, techniques based on image processing are
quicker and more accurate in comparison with the other methods. The aim of this study was
to use image-processing techniques to detect the levels of drowsiness in a driving simulator.
Methods: This study was conducted on five suburban drivers using a driving simulator based …
Abstract
Background:
Drowsiness is one of the underlying causes of driving accidents, which contribute, to many road fatalities annually. Although numerous methods have been developed to detect the level of drowsiness, techniques based on image processing are quicker and more accurate in comparison with the other methods. The aim of this study was to use image-processing techniques to detect the levels of drowsiness in a driving simulator.
Methods:
This study was conducted on five suburban drivers using a driving simulator based on virtual reality laboratory of Khaje-Nasir Toosi University of Technology in 2015 Tehran, Iran. The facial expressions, as well as location of the eyes, were detected by Violla-Jones algorithm. Criteria for detecting drivers’ levels of drowsiness by eyes tracking included eye blink duration blink frequency and PERCLOS that was used to confirm the results.
Results:
Eye closure duration and blink frequency have a direct ratio of drivers’ levels of drowsiness. The mean of squares of errors for data trained by the network and data into the network for testing, were 0.0623 and 0.0700, respectively. Meanwhile, the percentage of accuracy of detecting system was 93.
Conclusion:
The results showed several dynamic changes of the eyes during the periods of drowsiness. The present study proposes a fast and accurate method for detecting the levels of drivers’ drowsiness by considering the dynamic changes of the eyes.
ncbi.nlm.nih.gov
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