Facial Features Monitoring for Real Time Drowsiness Detection using Support Vector Machine

Fatigue among drivers is a major cause of road accidents every year in India. Lack of sound sleep for six to eight hours is one of the primary reasons behind this fatigue. Drivers with sleep deprivation can imbalance the reaction time and decision making when behind the wheels and this can increase the cause of accidents. This type of accidents is more likely to result in death or severe injury as they tend to be in high speed and because of the fact that driver has fallen asleep cannot apply brake or skew to avoid or reduce the impact. Therefore, it is highly essential to create a smart system which can spot and alert the driver of his/her condition. Although there are few solutions proposed in this direction, most of them have not been implemented successfully and many of them only remain in theory. In this research paper, we propose an efficient driver fatigue detection and alert system using mainly open-source technologies. We implement and test this system in real time and the results are highly encouraging compared to many existing systems.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-03-25
DOI
https://doi.org/10.5281/zenodo.15084877
Primary Topic
Sleep and Work-Related Fatigue
Type
article
Field-Weighted Citation Impact
0.00
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article

Facial Features Monitoring for Real Time Drowsiness Detection using Support Vector Machine

Zenodo (CERN European Organization for Nuclear Research)
Sleep and Work-Related Fatigue
article

Facial Features Monitoring for Real Time Drowsiness Detection using Support Vector Machine

article en

Abstract

Fatigue among drivers is a major cause of road accidents every year in India. Lack of sound sleep for six to eight hours is one of the primary reasons behind this fatigue. Drivers with sleep deprivation can imbalance the reaction time and decision making when behind the wheels and this can increase the cause of accidents. This type of accidents is more likely to result in death or severe injury as they tend to be in high speed and because of the fact that driver has fallen asleep cannot apply brake or skew to avoid or reduce the impact. Therefore, it is highly essential to create a smart system which can spot and alert the driver of his/her condition. Although there are few solutions proposed in this direction, most of them have not been implemented successfully and many of them only remain in theory. In this research paper, we propose an efficient driver fatigue detection and alert system using mainly open-source technologies. We implement and test this system in real time and the results are highly encouraging compared to many existing systems.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 46%
Sleep and Work-Related Fatigue
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