<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In recent years, with the improvement of the national economy, the penetration rate of automobiles has been increasing, and traffic accidents have also increased. Fatigue driving is the main factor in many traffic accidents. Fatigue driving can cause the driver’s inattention, slow response, and make wrong decisions on danger signals, which affect the driver’s personal safety. In modern development, driving safety is developing towards intelligence and safety. Therefore, the detection of driver fatigue has become a generally accepted demand. This paper proposes a method to calculate the threshold of blinking, which can detect the blinking state of the driver in real time through video. During the driving process, when the driver is in the closed eye state for a long time, an early warning is issued to avoid the accident. This paper uses Python language to achieve the first, through the digital image technology call Dlib open source library to detect 68 feature points of the face, and then measure the aspect ratio between the length and width of the human eye, and finally through the Kmeans clustering algorithm to collect the ratio The analysis yields the blink threshold. The experimental results show that the recognition rate is 92.5% when the video frame rate is 30, and the recognition accuracy is 92.5%. The experimental results show that the method designed in this paper can quickly detect the fatigue characteristics of the human eye, has a higher recognition rate and accuracy for fatigue driving, and helps reduce the occurrence of traffic accidents.</p></abstract>

In recent years, with the improvement of the national economy, the penetration rate of automobiles has been increasing, and traffic accidents have also increased. Fatigue driving is the main factor in many traffic accidents. Fatigue driving can cause the driver’s inattention, slow response, and make wrong decisions on danger signals, which affect the driver’s personal safety. In modern development, driving safety is developing towards intelligence and safety. Therefore, the detection of driver fatigue has become a generally accepted demand. This paper proposes a method to calculate the threshold of blinking, which can detect the blinking state of the driver in real time through video. During the driving process, when the driver is in the closed eye state for a long time, an early warning is issued to avoid the accident. This paper uses Python language to achieve the first, through the digital image technology call Dlib open source library to detect 68 feature points of the face, and then measure the aspect ratio between the length and width of the human eye, and finally through the Kmeans clustering algorithm to collect the ratio The analysis yields the blink threshold. The experimental results show that the recognition rate is 92.5% when the video frame rate is 30, and the recognition accuracy is 92.5%. The experimental results show that the method designed in this paper can quickly detect the fatigue characteristics of the human eye, has a higher recognition rate and accuracy for fatigue driving, and helps reduce the occurrence of traffic accidents.

Detection of Blink State Based on Fatigued Driving

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Detection of Blink State Based on Fatigued Driving"}

In recent years, with the improvement of the national economy, the penetration rate of automobiles has been increasing, and traffic accidents have also...

literature	Recognition rate
9	91.5%
14	83.7%
This article	92.5%

Experimental sample	Blink threshold	Blink times	Number of recognition	Recognition rate
Text person	5.1	100	90	90%
Public data set	5.1	255	236	92.5%

Detection of Blink State Based on Fatigued Driving

Published Online: Jan 27, 2020

Page range: 24 - 29

DOI: https://doi.org/10.21307/ijanmc-2019-067

Keywords
Blinking Algorithm, Fatigue Detection, Digital Image Processing, Clustering Algorithm, Key Points Of Human Eyes

© 2019 Lei Chao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

COMPARED WITH OTHER LITERATURE

COMPARED WITH PUBLIC DATASETS

Detection of Blink State Based on Fatigued Driving

Published Online: Jan 27, 2020

Page range: 24 - 29

DOI: https://doi.org/10.21307/ijanmc-2019-067

KeywordsBlinking Algorithm, Fatigue Detection, Digital Image Processing, Clustering Algorithm, Key Points Of Human Eyes

© 2019 Lei Chao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

COMPARED WITH OTHER LITERATURE

COMPARED WITH PUBLIC DATASETS

Keywords
Blinking Algorithm, Fatigue Detection, Digital Image Processing, Clustering Algorithm, Key Points Of Human Eyes