Motorist Solemness Monitoring System using Support Vector Machine

Chitta Venkata Ramya; Divve Meghana; Battina Pravallika Chowdary

Motorist Solemness Monitoring System using Support Vector Machine

June 2022

Vol-8, Issue-3

Paper ID: 17262

ISSN: 2395-4396

Downloads: 0

Abstract & Details

Research Area

Information Technology

Keywords

motorist(driver) drowsiness(solemness) detection visual behaviour eye aspect ratio mouth opening ratio nose length ratio

Abstract

Drowsy(solemness) driving is one of the major causes of road accidents and death. Hence, detection of driver’s fatigue and its indication is an active research area. Most of the conventional methods are either vehicle based, or behavioural based or physiological based. Few methods are intrusive and distract the driver, some require expensive sensors and data handling. Therefore, in this study, a low cost, real time driver’s drowsiness detection system is developed with acceptable accuracy. In the developed system, a webcam records the video and driver’s face is detected in each frame employing image processing techniques. Facial landmarks on the detected face are pointed and subsequently the eye aspect ratio, mouth opening ratio and nose length ratio are computed and depending on their values, drowsiness is detected based on developed adaptive thresholding. Machine learning algorithms have been implemented as well in an offline manner. A sensitivity of 95.58% and specificity of 100% has been achieved in Support Vector Machine based classification.

License

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Author Information

#	Name	Institute / Affiliation
1	Chitta Venkata Ramya	Vasireddy Venkatadri Institute of Technology
2	Divve Meghana	Vasireddy Venkatadri Institute of Technology
3	Battina Pravallika Chowdary	Vasireddy Venkatadri Institute of Technology

How to Cite

Use the following formats to cite this article in your research.

APA Style

Ramya, Chitta Venkata, Meghana, Divve, & Chowdary, Battina Pravallika (2022). Motorist Solemness Monitoring System using Support Vector Machine. International Journal of Advance Research and Innovative Ideas In Education, 8(3), 3577-3584.

MLA Style

Ramya, Chitta Venkata, et al. "Motorist Solemness Monitoring System using Support Vector Machine." International Journal of Advance Research and Innovative Ideas In Education, vol. 8, no. 3, 2022, pp. 3577-3584.

IEEE Style

Chitta Venkata Ramya, Divve Meghana, and Battina Pravallika Chowdary, "Motorist Solemness Monitoring System using Support Vector Machine," International Journal of Advance Research and Innovative Ideas In Education, vol. 8, no. 3, pp. 3577-3584, 2022.

Vancouver Style

Ramya Chitta Venkata, Meghana Divve, Chowdary Battina Pravallika. Motorist Solemness Monitoring System using Support Vector Machine. International Journal of Advance Research and Innovative Ideas In Education. 2022;8(3):3577-3584.

Harvard Style

Ramya, Chitta Venkata, Meghana, Divve, & Chowdary, Battina Pravallika (2022) 'Motorist Solemness Monitoring System using Support Vector Machine', International Journal of Advance Research and Innovative Ideas In Education, 8(3), pp. 3577-3584.

Chicago Style

Ramya, Chitta Venkata, Divve Meghana, and Battina Pravallika Chowdary. "Motorist Solemness Monitoring System using Support Vector Machine." International Journal of Advance Research and Innovative Ideas In Education 8, no. 3 (2022): 3577-3584.

Turabian Style

Ramya, Chitta Venkata, Divve Meghana, and Battina Pravallika Chowdary. "Motorist Solemness Monitoring System using Support Vector Machine." International Journal of Advance Research and Innovative Ideas In Education 8, no. 3 (2022): 3577-3584.