Real-Time Driver Drowsiness Detection Using Deep Learning and Heterogeneous Computing on Embedded System

2020 ◽  
pp. 86-97
Author(s):  
Shivam Khare ◽  
Sandeep Palakkal ◽  
T. V. Hari Krishnan ◽  
Chanwon Seo ◽  
Yehoon Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Embedded System ◽  
Real Time ◽  
Heterogeneous Computing ◽  
Drowsiness Detection ◽  
Driver Drowsiness

A Deep Learning Based Approach for Real-time Driver Drowsiness Detection

2021 ◽  
Author(s):  
Anis-Ul-Islam Rafid ◽  
Atiqul Islam Chowdhury ◽  
Amit Raha Niloy ◽  
Nusrat Sharmin
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Drowsiness Detection ◽  
Driver Drowsiness

scholarly journals Real-time Driver Drowsiness Detection using Deep Learning

2021 ◽  
Vol 12 (7) ◽  
Author(s):  
Md. Tanvir Ahammed Dipu ◽  
Syeda Sumbul Hossain ◽  
Yeasir Arafat ◽  
Fatama Binta Rafiq
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Drowsiness Detection ◽  
Driver Drowsiness

EEG-based System Using Deep Learning and Attention Mechanism for Driver Drowsiness Detection

2021 ◽  
Author(s):  
Miankuan Zhu ◽  
Haobo Li ◽  
Jiangfan Chen ◽  
Mitsuhiro Kamezaki ◽  
Zutao Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Attention Mechanism ◽  
Drowsiness Detection ◽  
Driver Drowsiness

scholarly journals Smart Real-Time Video Surveillance Platform for Drowsiness Detection Based on Eyelid Closure

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Muhammad Tayab Khan ◽  
Hafeez Anwar ◽  
Farman Ullah ◽  
Ata Ur Rehman ◽  
Rehmat Ullah ◽  
...  
Keyword(s):  
Real Time ◽  
Video Surveillance ◽  
Classification Accuracy ◽  
Eyelid Closure ◽  
Drowsiness Detection ◽  
Driver Drowsiness ◽  
Sobel Operator ◽  
The Face ◽  
Uniform Background ◽  
The Subject

We propose drowsiness detection in real-time surveillance videos by determining if a person’s eyes are open or closed. As a first step, the face of the subject is detected in the image. In the detected face, the eyes are localized and filtered with an extended Sobel operator to detect the curvature of the eyelids. Once the curves are detected, concavity is used to tell whether the eyelids are closed or open. Consequently, a concave upward curve means the eyelid is closed whereas a concave downwards curve means the eye is open. The proposed method is also implemented on hardware in order to be used in real-time scenarios, such as driver drowsiness detection. The evaluation of the proposed method used three image datasets, where images in the first dataset have a uniform background. The proposed method achieved classification accuracy of up to 95% on this dataset. Another benchmark dataset used has significant variations based on face deformations. With this dataset, our method achieved classification accuracy of 70%. A real-time video dataset of people driving the car was also used, where the proposed method achieved 95% accuracy, thus showing its feasibility for use in real-time scenarios.

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