Detecting driver drowsiness using total pixel algorithm

Adi, Kusworo; Widodo, Aris Puji; Widodo, Catur Edi; Putranto, Ari Bawono; Naqiyah, Salsabila; Aristia, Hilda Nurul

doi:10.1088/1742-6596/1217/1/012036

Cited by 2 publications

(6 citation statements)

References 4 publications

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“…Because each pixel value in an image is calculated by multiplying the number of pixels in a square by the number of pixels in that square, this method is fast [12]. The findings build on previous works using the total pixel method [13].…”

Section: Introductionmentioning

confidence: 52%

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Role of Deep Learning in Improving the Performance of Driver Fatigue Alert System

Fouad¹,

Labib²

2022

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Driver fatigue detection system aims to monitor the driver state. When detecting a fatigue caused by different attitudes other than normal driving habit, the system warns the driver that traveling should be interrupted. In this way, it helps the driver to make the right decision. The aim of this study is to prevent traffic accidents. The system analyzes any changes in the driver's eyes and mouth features in real time and warns the driver when necessary. The proposed system contains several stages to detect the driver's fatigue. First, the preprocessing stage; enhancement of the frames, determining the face, and cropping eyes and mouth of the driver was done. Then, dealing with feature extraction stage; the features concerning each frame was processed. Finally, two classification approaches were presented and a comparison between them was addressed. In the first approach, four traditional classifiers were applied; Diagonal Linear Discriminant Analysis (DiagLDA), Linear Support Vector Machine (LSVM), K-Nearest Neighbor (KNN), and Random Forest Classifier (RFC). The results show that two classifiers; KNN and RFC yield the highest average accuracy of 91.94% for all subjects presented in this paper. In the second approach, one model of deep learning neural network (CNN) was applied; "Resnet-50" model. The results also show that the proposed deep learning model yields a high average accuracy of 96.3889% for the same data. In general, the drowsiness and lost focus of drivers with high accuracy have been detected with the developed image processing based system, which makes it practicable and reliable for real-time applications.

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Section: Introductionmentioning

confidence: 52%

“…Spatial frequency and light intensity of an object are shown as a spatial distribution on the image. The data were represented using components such as edges, brightness, and color [13,14]. Digital image consists of a number of rows of bits which represent real and complex values [15,16].…”

Section: Introductionmentioning

confidence: 99%

Role of Deep Learning in Improving the Performance of Driver Fatigue Alert System

Fouad¹,

Labib²

2022

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“…This method has the advantage that the computation is high-speed because it only depends on the number of pixels in a square instead of every pixel value of an image ( 12 ). This research is a development of previous research using the total pixel algorithm ( 13 ).…”

Section: Introductionmentioning

confidence: 99%

“…The image represents the spatial distribution of physical quantities such as light intensity and spatial frequency of an object. The information was represented by components such as brightness, color, and edges ( 13 , 14 ). The digital image is an array containing real and complex values represented by specific rows of bits ( 15 , 16 ).…”

Section: Introductionmentioning

confidence: 99%

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Monitoring System of Drowsiness and Lost Focused Driver Using Raspberry Pi

Adi

Widodo

et al. 2020

ijph

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Background: Drowsiness condition is one of the significant factors often encountered when an accident occurs. We aimed to detect a method to prevent accidents caused by drowsiness and lost a focused driver. Methods: The image processing technique has been capable of detecting the characteristic of drowsiness and lost focus driver in real-time using Raspberry Pi. Video samples were processed using the Haar Cascade Classifier method to identify areas of the face, eyes, and mouth so that drowsy conditions. The methods can be determined based on the bject detected. Results: Two parameters were determined, the lost focused and drowsiness driver. The highest accuracy value for driver lost focused detection was 88.00%, while the highest accuracy value for drowsiness driver detection was 90.40%. Conclusion: In general, a system developed with image processing methods has been able to monitor the drowsiness and lost focused drivers with high accuracy. This system still needs improvements to increase performance.

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Detecting driver drowsiness using total pixel algorithm

Cited by 2 publications

References 4 publications

Role of Deep Learning in Improving the Performance of Driver Fatigue Alert System

Role of Deep Learning in Improving the Performance of Driver Fatigue Alert System

Monitoring System of Drowsiness and Lost Focused Driver Using Raspberry Pi

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