Digital Image Processing

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Drowsy Driver Alert System Using Image Processing


Affiliations
1 Bannari Amman Institute of Technology, Sathyamangalam, TN, India
2 Bannari Amman Institute of Technology, Sathyamangalam, TN, India
     

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A non-intrusive machine vision based concept is adopted in the development of Drowsy Driver Alert System. The system uses a small monochrome security camera that points directly towards the driver's face and monitors the driver's eyes in order to detect fatigue. The aim of this work is to develop a prototype drowsiness detection system. This paper describes about how to monitor the eye and detect for the eye closure. The system deals with information obtained for the binary version of the image to find the edges of the face, which narrows the area of where the eyes may exist. By considering the fact that the eye regions in the face reflect greater intensity changes than other parts, the eyes are located by finding the significant intensity changes in the face. The intensity changes in the eyes area determine whether eyes are open or closed. If the eyes are found closed for five consecutive frames, the driver is falling asleep and issues a warning signal.

Keywords

Drowsiness Detection, Feature Extraction, Frame, Image Segmentation, Non-Invasive System.
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  • Drowsy Driver Alert System Using Image Processing

Abstract Views: 252  |  PDF Views: 3

Authors

K. P. Sampoornam
Bannari Amman Institute of Technology, Sathyamangalam, TN, India
N. Saranya
Bannari Amman Institute of Technology, Sathyamangalam, TN, India

Abstract


A non-intrusive machine vision based concept is adopted in the development of Drowsy Driver Alert System. The system uses a small monochrome security camera that points directly towards the driver's face and monitors the driver's eyes in order to detect fatigue. The aim of this work is to develop a prototype drowsiness detection system. This paper describes about how to monitor the eye and detect for the eye closure. The system deals with information obtained for the binary version of the image to find the edges of the face, which narrows the area of where the eyes may exist. By considering the fact that the eye regions in the face reflect greater intensity changes than other parts, the eyes are located by finding the significant intensity changes in the face. The intensity changes in the eyes area determine whether eyes are open or closed. If the eyes are found closed for five consecutive frames, the driver is falling asleep and issues a warning signal.

Keywords


Drowsiness Detection, Feature Extraction, Frame, Image Segmentation, Non-Invasive System.

References