A novel approach for driver drowsiness detection using deep learning

2021 ◽  
Author(s):  
M. N. Kavitha ◽  
S. S. Saranya ◽  
K. Dhanush Adithyan ◽  
R. Soundharapandi ◽  
A. S. Vignesh
Keyword(s):  
Deep Learning ◽  
Drowsiness Detection ◽  
Driver Drowsiness ◽  
Novel Approach

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

Driver Drowsiness Detection Using Deep Learning

2021 ◽  
Author(s):  
Rupali Pawar ◽  
Saloni Wamburkar ◽  
Rutuja Deshmukh ◽  
Nikita Awalkar
Keyword(s):  
Deep Learning ◽  
Drowsiness Detection ◽  
Driver Drowsiness

Driver Drowsiness Detection using Deep Learning

2021 ◽  
Author(s):  
Yeresime Suresh ◽  
Rashi Khandelwal ◽  
Matam Nikitha ◽  
Mohammed Fayaz ◽  
Vinaya Soudhri
Keyword(s):  
Deep Learning ◽  
Drowsiness Detection ◽  
Driver Drowsiness

scholarly journals A Review of EEG Signal Features and their Application in Driver Drowsiness Detection Systems

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3786
Author(s):  
Igor Stancin ◽  
Mario Cifrek ◽  
Alan Jovic
Keyword(s):  
Deep Learning ◽  
Difficult Problem ◽  
Learning Approaches ◽  
Eeg Signal ◽  
Drowsiness Detection ◽  
Detection Systems ◽  
Driver Drowsiness ◽  
Signal Features ◽  
Challenges And Opportunities ◽  
Electroencephalogram Eeg

Detecting drowsiness in drivers, especially multi-level drowsiness, is a difficult problem that is often approached using neurophysiological signals as the basis for building a reliable system. In this context, electroencephalogram (EEG) signals are the most important source of data to achieve successful detection. In this paper, we first review EEG signal features used in the literature for a variety of tasks, then we focus on reviewing the applications of EEG features and deep learning approaches in driver drowsiness detection, and finally we discuss the open challenges and opportunities in improving driver drowsiness detection based on EEG. We show that the number of studies on driver drowsiness detection systems has increased in recent years and that future systems need to consider the wide variety of EEG signal features and deep learning approaches to increase the accuracy of detection.

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

scholarly journals Deep Learning for Transient Image Reconstruction from ToF Data

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1962
Author(s):  
Enrico Buratto ◽  
Adriano Simonetto ◽  
Gianluca Agresti ◽  
Henrik Schäfer ◽  
Pietro Zanuttigh
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Light Response ◽  
Real Data ◽  
Depth Image ◽  
Learning Approach ◽  
Multiple Reflections ◽  
Noisy Input ◽  
Novel Approach ◽  
Incoming Light

In this work, we propose a novel approach for correcting multi-path interference (MPI) in Time-of-Flight (ToF) cameras by estimating the direct and global components of the incoming light. MPI is an error source linked to the multiple reflections of light inside a scene; each sensor pixel receives information coming from different light paths which generally leads to an overestimation of the depth. We introduce a novel deep learning approach, which estimates the structure of the time-dependent scene impulse response and from it recovers a depth image with a reduced amount of MPI. The model consists of two main blocks: a predictive model that learns a compact encoded representation of the backscattering vector from the noisy input data and a fixed backscattering model which translates the encoded representation into the high dimensional light response. Experimental results on real data show the effectiveness of the proposed approach, which reaches state-of-the-art performances.

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