Synchronous System for Driver Drowsiness Detection Using Convolutional Neural Network, Computer Vision and Android Technology

Author(s):  
Purvika Bajaj ◽  
Renesa Ray ◽  
Shivani Shedge ◽  
Sagar Jaikar ◽  
Pranav More
Author(s):  
Sara Houshmand ◽  
Reza Kazemi ◽  
Hamed Salmanzadeh

A significant number of fatal accidents are caused by drowsy drivers worldwide. Driver drowsiness detection based on electroencephalography (EEG) signals has high accuracy and is known as a reference method for evaluating drowsiness. Among brain waves, EEG alpha spindle activity is a silent feature of decreasing alertness levels. In this paper, based on the detection of EEG alpha spindles, a novel driver drowsiness detection method is presented. The EEG spindles were detected using Continuous Wavelet Transform (CWT) analysis and the Morlet function. To do so, the signal is divided into 30-s epochs, and the observer rating of drowsiness determines the drowsiness level in each epoch. Tests were conducted on 17 healthy males in a driving simulator with a monotonous driving scenario. The Convolutional Neural Network (CNN) is used for classifying EEG signals and automatically learns features of the early drowsy state. The subject-independent classification results for single-channel P4 show 94% accuracy.


SinkrOn ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 202
Author(s):  
Hotman Parsaoran Tampubolon

Abstrak— At present technological developments, especially in the field of computer vision, are showing significant performance such as the application of convolutional neural networks that have a very high degree of accuracy, for example improving video quality which recently has image restoration such as super resolution (VSR) thanks to deep learning with the aim of helping produce better visual videos. The use of video cameras for mobile devices is now increasingly highly developed. Nowadays mobile devices are experiencing a rapid increase in quality especially in cameras. However, physical limitations such as the small sensor size, compact lens and the lack of supporting hardware can prevent cellular devices from achieving good video camera quality results. For that many method approaches are applied, one of which is the CNN (Convolutional Neural Network) method. This method can improve the image of video recordings that have poor quality. Keywords—Convolutional neural network, computer vision, Improved video quality ;


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