Driver Drowsiness Detection and Alert System using Python and OpenCV

2021 ◽  
Author(s):  
BRINDHA HARINI R ◽  
YAMINI R
Keyword(s):  
Visual Information ◽  
Facial Recognition ◽  
Transportation Safety ◽  
Alert System ◽  
Safety Measures ◽  
Drowsiness Detection ◽  
Driver Drowsiness ◽  
Accident Rates ◽  
The One ◽  
Fatigue Detection

Abstract Driver Drowsiness is the one of the reasons for increase in accident rates. Various facial recognition methods have been proposed to detect and alert the driver in-order to avoid accidents. Hence, this system is proposed to reduce the number of accidents due to drivers fatigue and hence increase the transportation safety. This system deals with automatic driver drowsiness detection based on visual information captured by the system.The driver is lively captured after which the images are further processed, and the fatigue is checked for. It creates an alarm for the driver immediately in case of fatigue detection, also an implementation to alert the vehicles owner and others concerned about the safety are alerted as well. The system enhances the safety measures by which accidents due to drivers drowsiness can be minimized.

Real-Time Warning System for Driver Drowsiness Detection Using Visual Information

2009 ◽  
Vol 59 (2) ◽  
pp. 103-125 ◽  
Author(s):  
Marco Javier Flores ◽  
José María Armingol ◽  
Arturo de la Escalera
Keyword(s):  
Real Time ◽  
Visual Information ◽  
Warning System ◽  
Drowsiness Detection ◽  
Driver Drowsiness

Driver Drowsiness Detection and Alert System Using Computer Vision

2021 ◽  
pp. 1-10
Author(s):  
K. Vinutha ◽  
N. Ashwini ◽  
Amrit Raj ◽  
Jayam Sukruth ◽  
M. Praneeth ◽  
...  
Keyword(s):  
Computer Vision ◽  
Alert System ◽  
Drowsiness Detection ◽  
Driver Drowsiness

scholarly journals Driver Drowsiness Detection System

2021 ◽  
Vol 9 (VII) ◽  
pp. 1226-1231
Author(s):  
Kiranmayee V
Keyword(s):  
Visual Information ◽  
Driver Assistance System ◽  
Detection System ◽  
Assistance System ◽  
Advanced Driver Assistance System ◽  
Driver Assistance ◽  
Drowsiness Detection ◽  
Driver Drowsiness ◽  
Drugs And Alcohol ◽  
Eye Closure

Drowsiness of drivers are among the critical reasons for accidents. This can be a relatively smaller number still, as among the multiple causes that can lead to an accident. Drowsiness, in general, is not easy to measure unlike drugs and alcohol, which have tests and indicators that are available easily. In this paper, we are presenting a module for Advanced Driver Assistance System (ADAS) to reduce drowsiness related accidents. The system deals with automatic driver drowsiness detection based on visual information. We propose an algorithm to track, analyze and locate both the drivers eyes and face to measure PERCLOS, a scientifically supported measure of drowsiness asso- ciated with slow eye closure.

scholarly journals Automatic Driver Drowsiness Detection Based on Visual Information and Artificial Intelligence

2019 ◽  
pp. 294-301
Author(s):  
Yeshwanth Rao Bhandayker
Keyword(s):  
Artificial Intelligence ◽  
Visual Information ◽  
Road Crashes ◽  
Drowsiness Detection ◽  
Driver Drowsiness ◽  
Transport Safety ◽  
Slow Moving ◽  
Eye Closure

Drowsiness as well as Tiredness of motorists is amongst the considerable root causes of road crashes. Yearly, they raise the quantities of deaths as well as fatalities injuries globally. In this paper, a module for Advanced Motorist Aid System (ADAS) is presented to lower the number of crashes as a result of chauffeurs tiredness as well as therefore in-crease the transport safety; this system manages automatic chauffeur drowsiness detection based on aesthetic info and also Artificial Intelligence. We suggest a formula to find, track, and evaluate both the vehicle driver’s deal with and also eyes to determine PERCLOS, a scientifically supported measure of sleepiness related to slow-moving eye closure.

scholarly journals Bio-Inspired Modality Fusion for Active Speaker Detection

2021 ◽  
Vol 11 (8) ◽  
pp. 3397
Author(s):  
Gustavo Assunção ◽  
Nuno Gonçalves ◽  
Paulo Menezes
Keyword(s):  
Superior Colliculus ◽  
Visual Information ◽  
Human Beings ◽  
Validation Process ◽  
Detection Approach ◽  
Wide Range ◽  
Speaker Detection ◽  
The One ◽  
The Brain ◽  
Fusion Ability

Human beings have developed fantastic abilities to integrate information from various sensory sources exploring their inherent complementarity. Perceptual capabilities are therefore heightened, enabling, for instance, the well-known "cocktail party" and McGurk effects, i.e., speech disambiguation from a panoply of sound signals. This fusion ability is also key in refining the perception of sound source location, as in distinguishing whose voice is being heard in a group conversation. Furthermore, neuroscience has successfully identified the superior colliculus region in the brain as the one responsible for this modality fusion, with a handful of biological models having been proposed to approach its underlying neurophysiological process. Deriving inspiration from one of these models, this paper presents a methodology for effectively fusing correlated auditory and visual information for active speaker detection. Such an ability can have a wide range of applications, from teleconferencing systems to social robotics. The detection approach initially routes auditory and visual information through two specialized neural network structures. The resulting embeddings are fused via a novel layer based on the superior colliculus, whose topological structure emulates spatial neuron cross-mapping of unimodal perceptual fields. The validation process employed two publicly available datasets, with achieved results confirming and greatly surpassing initial expectations.

scholarly journals Driver Drowsiness Estimation Based on Factorized Bilinear Feature Fusion and a Long-Short-Term Recurrent Convolutional Network

Information ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 3
Author(s):  
Shuang Chen ◽  
Zengcai Wang ◽  
Wenxin Chen
Keyword(s):  
Short Term Memory ◽  
Feature Fusion ◽  
Detection Methods ◽  
Video Frame ◽  
Estimation Model ◽  
Short Term ◽  
Convolutional Network ◽  
Drowsiness Detection ◽  
Driver Drowsiness ◽  
Time Information

The effective detection of driver drowsiness is an important measure to prevent traffic accidents. Most existing drowsiness detection methods only use a single facial feature to identify fatigue status, ignoring the complex correlation between fatigue features and the time information of fatigue features, and this reduces the recognition accuracy. To solve these problems, we propose a driver sleepiness estimation model based on factorized bilinear feature fusion and a long- short-term recurrent convolutional network to detect driver drowsiness efficiently and accurately. The proposed framework includes three models: fatigue feature extraction, fatigue feature fusion, and driver drowsiness detection. First, we used a convolutional neural network (CNN) to effectively extract the deep representation of eye and mouth-related fatigue features from the face area detected in each video frame. Then, based on the factorized bilinear feature fusion model, we performed a nonlinear fusion of the deep feature representations of the eyes and mouth. Finally, we input a series of fused frame-level features into a long-short-term memory (LSTM) unit to obtain the time information of the features and used the softmax classifier to detect sleepiness. The proposed framework was evaluated with the National Tsing Hua University drowsy driver detection (NTHU-DDD) video dataset. The experimental results showed that this method had better stability and robustness compared with other methods.

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