Real Time Driver Fatigue Detection Based on SVM Algorithm

Savaş, Burcu Kır; Becerikli, Yaşar

doi:10.1109/ceit.2018.8751886

Cited by 29 publications

(22 citation statements)

References 9 publications

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“…A driver fatigue recognition scheme in real-time grounded on the SVM algorithm is projected. Fatigue discovery chiefly emphasizes drivers' facial appearances and behaviors [20]. The model that employs the infrared videos aimed at perceiving and a method using CNN aimed at identifying eye state was projected.…”

Section: Literature Reviewmentioning

confidence: 99%

Eye blink detection using CNN to detect drowsiness level in drivers for road safety

Vishesh¹,

Raghavendra²,

Jankatti³

et al. 2021

IJEECS

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<span><span>Blinking is a regular bodily function and it is the semiautomatic fast closing of the eyelid. A specific blink is examined by dynamic folding of the eyelid. It is a vital function of the eye which helps in spread of tears across and eliminates irritants from the shallow of cornea. In this research work we made use of convolution neural network, the deep learning concepts and image processing to detect drowsiness level in drivers. To train the blink detection model the mobilenet V2 is used as base. The loss function used for training was RMSprop and the optimizer is binary cross entropy. The dlib facial landmark was exploited to perceive and pre-process the detected faces. The dataset used for the training model is selected from the “Xiaoyang Tan” of nanjing university of aeronautics and astronautics. Based on the experimental outcome the projected method achieves an accuracy of 97%. The prototype developed serves as a base for further development of this process to achieve better road safety</span>.</span>

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Section: Literature Reviewmentioning

confidence: 99%

Eye blink detection using CNN to detect drowsiness level in drivers for road safety

Vishesh¹,

Raghavendra²,

Jankatti³

et al. 2021

IJEECS

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“…In our previous work, we [7], [17] designed a two-label (fatigue/not fatigue) system for driver fatigue detection and SVM and Adaboost algorithms were used for classification. In this study, driver fatigue detection is performed with Multi-task ConNN using raw data obtained from different data sets in the literature.…”

Section: Proposed Approachmentioning

confidence: 99%

“…In the study, the segmented region having the maximum area within the mouth region classifies the frame on the YawDD dataset as a stretch frame. Kır Savaş et al [17] tried to detect driver fatigue using the SVM algorithm. In their studies, PERCLOS uses the number of yawns, the inner region of the mouth opening and the number of blink for the determination of driver fatigue on their own dataset.…”

Section: Introductionmentioning

confidence: 99%

Real Time Driver Fatigue Detection System Based on Multi-Task ConNN

2020

Self Cite

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Changes and progresses in information technologies have played an important role in the development of intelligent vehicle systems in recent years. Driver fatigue is an important factor in vehicle accidents. For this reason, traffic accidents involving driver fatigue and driver carelessness have been followed by researchers. In this article, a Multi-tasking Convulational Neural Network (ConNN *) model is proposed to detect driver drowsiness/fatigue. Eye and mouth characteristics are utilized for driver's behavior model. Changes to these characteristics are used to monitor driver fatigue. With the proposed Multi-task ConNN model, unlike the studies in the literature, both mouth and eye information are classified into a single model at the same time. Driver fatigue is determined by calculating eyes' closure duration/Percentage of eye closure (PERCLOS) and yawning frequency/frequency of mouth (FOM). In this study, the fatigue degree of the driver is divided into 3 classes. The proposed model achieved 98.81% fatigue detection on YawdDD and NthuDDD dataset. The success of the model is presented comparatively. INDEX TERMS Convolutional neural network, driver fatigue detection, PERCLOS, FOM. * Convulational Neural Network has been abbreviated as ConNN, not CNN or CoNN, CNN has been used as the abbreviation of Celluar Neural Network and CoNN has been used as the abbreviation of Cooperative neural networks in the leterature as a long time. The associate editor coordinating the review of this manuscript and approving it for publication was Zahid Akhtar .

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“…Next, we extract the left and right eye coordinates from the facial landmarks of the face region and compute the eye aspect ratio for each eye. The Eye Aspect Ratio or EAR is calculated in three steps:  The two pairs of vertical eye landmarks are taken and the Euclidean distance between them is computed by: V1 = dist.euclidean(eye [2],eye [6]) V2 = dist.euclidean(eye [3],eye [5])  The horizontal eye landmarks are taken and the Euclidean distance between them is computed by: H = dist.euclidean(eye [1],eye [4]) Finally, the eye aspect ratio is calculated by (V1 + V2) / (2.0 * H) as shown by Fig.3.…”

Section: Figure 2 Flowchart Of the Algorithmmentioning

confidence: 99%

“…This is because it neither relies on any external factor that could lead to a false positive nor does it require any physical connections to the driver that could distract the driver. The computer vision domain uses a variety of machine learning algorithms to determine drowsiness, such as the Support Vector Machine (SVM) algorithm that classifies objects by separating data items [3]. It detects the eyes and other facial features using a dataset but gives less accurate results and has a higher error rate, especially in large or noisy datasets.…”

mentioning

confidence: 99%

Driver Drowsiness Detection System using Machine Learning Algorithms

Ramalingam¹,

Shivani²,

Aditya³

2020

IJRTE

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Road crashes are the most common forms of accidents and deaths worldwide, and the significant reasons for these accidents are usually drunken, drowsiness and reckless behaviour of the driver. According to the World Health Organization, road traffic injuries have risen to 1.25 billion worldwide, which makes driver drowsiness detection a major potential area to avert numerous sleep-induced road accidents. This project proposes an idea to detect drowsiness using machine learning algorithms, hence alarming the driver in real-time to prevent a collision. The model uses the Haar Cascade algorithm, along with the OpenCV library to monitor the real-time video of the driver and to detect the eyes of the driver. The system uses the Eye Aspect Ratio (EAR) concept to determine if the eyes are open or closed. We also feed a data-set file consisting of the facial features data-points to train the machine learning algorithm. The model inspects each frame of the video, which helps to recognize the state of the driver. Furthermore, a Raspberry Pi single-board computer, combined with a camera module and an alarm system, facilitates the project to emulate a compact drowsiness detection system suitable for different automobiles.

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Real Time Driver Fatigue Detection Based on SVM Algorithm

Cited by 29 publications

References 9 publications

Eye blink detection using CNN to detect drowsiness level in drivers for road safety

Eye blink detection using CNN to detect drowsiness level in drivers for road safety

Real Time Driver Fatigue Detection System Based on Multi-Task ConNN

Driver Drowsiness Detection System using Machine Learning Algorithms

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