A Deep Learning Approach to Detect Drowsy Drivers in Real Time

Pinto, A.C.V.; Bhasi, Mohit; Bhalekar, Durvesh; Hegde, Pradyoth; Koolagudi, Shashidhar G.

doi:10.1109/indicon47234.2019.9030305

Cited by 13 publications

(7 citation statements)

References 7 publications

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“…The authors successfully simulated the system for real-time testing using a laptop webcam and achieved a detection accuracy of 98.64%. Also, Pinto et al [17] proposed their DDD system using the CEW dataset with 93.3% accuracy. A CNN is used to analyze the state of the eye after detecting the driver's face by the Histogram of Oriented Gradients(HOG) algorithm.…”

Section: Image-based Systemsmentioning

confidence: 99%

“…The different style of drowsiness actions for each driver is the reason behind the complexity of DDD systems. For that, building a system that depends on only yawning detection [13,[19][20][21]23] or eye state recognition [15][16][17]22] cannot be used as an effective real-life DDD system.…”

Section: Nthu-dddmentioning

confidence: 99%

“…Some studies [13][14][15][16][17][18] did not apply sequence learning by depending only on the information of each image frame separately and ignoring the temporal features encoded between adjacent frames. For that, it will be hard to distinguish between yawning and a wide-open mouth for talking and singing.…”

Section: Without Any Sequence Learning For the Drowsiness Actionsmentioning

confidence: 99%

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A Lightweight Driver Drowsiness Detection System Using 3DCNN With LSTM

Alameen¹,

Alhothali²

2023

Computer Systems Science and Engineering

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Today, fatalities, physical injuries, and significant economic losses occur due to car accidents. Among the leading causes of car accidents is drowsiness behind the wheel, which can affect any driver. Drowsiness and sleepiness often have associated indicators that researchers can use to identify and promptly warn drowsy drivers to avoid potential accidents. This paper proposes a spatiotemporal model for monitoring drowsiness visual indicators from videos. This model depends on integrating a 3D convolutional neural network (3D-CNN) and long short-term memory (LSTM). The 3DCNN-LSTM can analyze long sequences by applying the 3D-CNN to extract spatiotemporal features within adjacent frames. The learned features are then used as the input of the LSTM component for modeling high-level temporal features. In addition, we investigate how the training of the proposed model can be affected by changing the position of the batch normalization (BN) layers in the 3D-CNN units. The BN layer is examined in two different placement settings: before the non-linear activation function and after the non-linear activation function. The study was conducted on two publicly available drowsy drivers datasets named 3MDAD and YawDD. 3MDAD is mainly composed of two synchronized datasets recorded from the frontal and side views of the drivers. We show that the position of the BN layers increases the convergence speed and reduces overfitting on one dataset but not the other. As a result, the model achieves a test detection accuracy of 96%, 93%, and 90% on YawDD, Side-3MDAD, and Front-3MDAD, respectively.

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Section: Image-based Systemsmentioning

confidence: 99%

Section: Nthu-dddmentioning

confidence: 99%

Section: Without Any Sequence Learning For the Drowsiness Actionsmentioning

confidence: 99%

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A Lightweight Driver Drowsiness Detection System Using 3DCNN With LSTM

Alameen¹,

Alhothali²

2023

Computer Systems Science and Engineering

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“…Our framework can be utilized to give cautions appropriately to fatigue drivers, which will diminish and forestall the event of car crashes. Aldila Riztiane et al [11] proposed a framework that planned to alarm drivers so they can be advised to pull over and quit driving in a lazy state. The application "Driver Drowsiness Detection" uses Haar-course Detection just as layout coordinating in OpenCV to recognize and follow the eyes utilizing the front camera of an Android gadget.…”

Section: Fig 2 Drowsiness Detection [7]mentioning

confidence: 99%

Real time Drowsy Driver Detection using Polynomial Kernel based Support Vector Machine

Ahirwar¹

2021

IJRASET

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Casualty increases from road accidents day by day. There are so many reasons that accident causes and mostly due to human errors. Driver drowsiness is one of them. A small drowsiness may turn it into a big accident that resulted heavy casualties. If any of the system automatically detects the driver’s drowsiness and alert at real time may secure many lives. Drowsiness can be recognized by different situations such as by opening full mouth, by closing both the eyes and a combination of both. This may advised not to drive at drowsy state. There are various techniques through which drowsiness can be detected at real time but accuracy matters. OpenCV is a highly utilized open source computer vision library through which facial features can be recognized effectively. Polynomial kernel based support vector machine (SVM) is an advanced classification technique through which drowsiness can be classified from face. SVM is advanced machine learning approach through which linear and non-linear data can be classified with higher level of accuracy. System pertained 96.17 % of accuracy. Polynomial kernel is useful for non-linear data separation. Here system classifies the expressional features of face and result accordingly for drowsiness detection. Keywords: Support Vector Machine (SVM), OpenCV, Machine Learning, Non-Linear SVM Model, Drowsiness Detection, Face Detection, Computer Vision.

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“…Since the size of the utilized model is small, the obtained results are not high, and the obtained rate of accuracy is 81 %. Another real-time driver monitoring model was presented in [15], in which the method of Histogram of Oriented Gradients (HOG) is used for detecting the driver's face from the acquired video frames. Then, the ensemble of regression trees is used for obtaining bounding boxes for the eyes.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Implementation of driver's drowsiness assistance model based on eye movements detection

Waleed

Abbas

Hasan

2020

EEJET

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Annually, lots of persons are carrying lifelong disabilities or losing their lives owing to fatal accidents on the road. In addition to mechanical failures and people errors, driver's drowsiness represents one of the fundamental reasons for fatal accidents on the road. When drivers feel drowsy, lots of physiological and behavioral symptoms appear such as changes in the waves of the human brain, changes in facial expressions, variations in eye activity, decreasing head movements, etc. Therefore, there is a significant necessity to provide developed models of driver's drowsiness detection that exploit these symptoms for reducing accidents by warning drivers of drowsiness and fatigue. This paper concentrates on proposing a driver's drowsiness assistance model to monitor and alarm drivers by utilizing a behavioral-based method (eye movements detection method). In the proposed method of detecting eye movements (closed/opened), the Advanced Local Binary Pattern (Advanced LBP) is used, in which the descriptors are utilized to represent eye images to extract the tissue features of different persons in the driving car to see if the driver is in a drowsy state or not and this occurs after recording the driver's video and detecting the eyes of the driver. To extract the features in this way, the image of the eyes is divided into small regions through the Advanced LBP and sequenced into a single feature vector, where this method is used to determine the similarity features in the training group and to classify the eye image. The Naive Bayes classifier (NB) and Support Vector Machine (SVM) are utilized for giving good accuracy. The results indicate that the system has a high accuracy rate compared with the other existing methods, where the accuracy rate of NB and SVM using an eye detection dataset with training 90 % and testing 10% is 96 % and 97 %, respectively

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A Deep Learning Approach to Detect Drowsy Drivers in Real Time

Cited by 13 publications

References 7 publications

A Lightweight Driver Drowsiness Detection System Using 3DCNN With LSTM

A Lightweight Driver Drowsiness Detection System Using 3DCNN With LSTM

Real time Drowsy Driver Detection using Polynomial Kernel based Support Vector Machine

Implementation of driver's drowsiness assistance model based on eye movements detection

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