Driver Drowsiness Detection using OpenCV , Python

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Driver Drowsiness detection using OpenCV and Python

Platform : Python

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A new approach towards automobile safety and security with autonomous region primarily based automatic automotive system is projected during this conception. we have a tendency to propose 3 distinct however closely connected ideas viz. a Drowsy Driver Detection system and a traffic detection system with external vehicle intrusion dodging primarily based conception. In recent time’s automobile fatigue connected crashes have very enlarged. so as to attenuate these problems, we’ve incorporated driver alert system by watching each the driver’s eyes still as sensing still because the driver state of affairs based  primarily based native setting recognition based AI system is projected.


Nowadays, more and more professions require long-term concentration. Drivers must keep a close eye on the road, so they can react to sudden events immediately. Driver fatigue often becomes a direct cause of many traffic accidents. Therefore, there is a need to develop the systems that will detect and notify a driver of her/him bad psychophysical condition, which could significantly reduce the number of fatigue-related car accidents. However, the development of such systems encounters many difficulties related to fast and proper recognition of a driver’s fatigue symptoms. One of the technical possibilities to implement driver drowsiness detection systems is to use the vision-based approach. This article presents the currently used driver drowsiness detection systems. Here we are detecting the driver drowsiness by estimating vision system of him .



Driver drowsiness detection is a car safety technology which prevents accidents when the driver is getting drowsy. Various studies have suggested that around 20% of all road accidents are fatigue-related, up to 50% on certain roads. Driver fatigue is a significant factor in a large number of vehicle accidents. Recent statistics estimate that annually 1,200 deaths and 76,000 injuries can be attributed to fatigue related crashes. The development of technologies for detecting or preventing drowsiness at the wheel is a major challenge in the field of accident avoidance systems. Because of the hazard that drowsiness presents on the road, methods need to be developed for counteracting its affects. Driver inattention might be the result of a lack of alertness when driving due to driver drowsiness and distraction. Driver distraction occurs when an object or event draws a person’s attention away from the driving task. Unlike driver distraction, driver drowsiness involves no triggering event but, instead, is characterized by a progressive withdrawal of attention from the road and traffic demands. Both driver drowsiness and distraction, however, might have the same effects, i.e., decreased driving performance, longer reaction time, and an increased risk of crash involvement. shows the block diagram of overall system. Based on Acquisition of video from the camera that is in front of driver perform real-time processing of an incoming video stream in order to infer the driver’s level of fatigue if the drowsiness is Estimated then it will give the alert by sensing the eyes. 


Here we are used SVM(support vector machine) to classify the components in the input video. While cropping the region of interest components in the video is not accurate. Sometimes it will show regions wrong. To sense the eyes first we have to create boundary boxes for that and a classification algorithm. The algorithm of SVM will not support. 


There are several different algorithms and methods for eye tracking, and monitoring. Most of them in some way relate to features of the eye (typically reflections from the eye) within a video image of the driver. The original aim of this project was to use the retinal reflection as a means to finding the eyes on the face, and then using the absence of this reflection as a way of detecting when the eyes are closed. Applying this algorithm on consecutive video frames may aid in the calculation of eye closure period. Eye closure period for drowsy drivers are longer than normal blinking. It is also very little longer time could result in severe crash. So we will warn the driver as soon as closed eye is detected.


driver drowsiness using python


  • Region of interest is clear to identify
  • Bounding box creation and tracking


  • Real time tracking applications
  • Classification of small particles


  • Camera
  • Personal computer


  • Python 2.7 or above versions
  • Anaconda software 


The driver abnormality monitoring system developed is capable of detecting drowsiness, drunken and reckless behaviours of driver in a short time. The Drowsiness Detection System developed based on eye closure of the driver can differentiate normal eye blink and drowsiness and detect the drowsiness while driving. The proposed system can prevent the accidents due to the sleepiness while driving. The system works well even in case of drivers wearing spectacles and even under low light conditions if the camera delivers better output. Information about the head and eyes position is obtained through various self-developed image processing algorithms. During the monitoring, the system is able to decide if the eyes are opened or closed. When the eyes have been closed for too long, a warning signal is issued. processing judges the driver’s alertness level on the basis of continuous eye closures.


[1] Miaou, “Study of Vehicle Scrap page Rates,” Oak Ridge National Laboratory, Oak Ridge, TN,, S.P.,April 2012.

[2] Wreggit, S. S., Kim, C. L., and Wierwille, W. W., Fourth Semi-Annual Research Report”, Research on Vehicle-Based Driver Status Performance Monitoring”, Blacksburg, VA: Virginia Polytechnic Institute and State University, ISE Department, January 2013.

[3] Bill Fleming, “New Automotive Electronics Technologies”, International Conference on Pattern Recognition, pp. 484- 488,December 2012.

[4] Ann Williamson and Tim Chamberlain,“Review of on-road driver fatigue monitoring devices”, NSW Injury Risk Management Research Centre, University of New South Wales, , July 2013.

[5] E. Rogado, J.L. García, R. Barea, L.M. Bergasa, Member IEEE and E. López, February, 2013, “Driver Fatigue Detection System”, Proceedings of the IEEE International Conference on Robotics and Biometics, Bangkok, Thailand.

[6] Boon-Giin Lee and Wan-Young Chung, Member IEEE, “Driver Alertness Monitoring Using Fusion of Facial Features and Bio-Signals”, IEEE Sensors Journal, VOL. 12, NO. 7, July 2012.

[7] H. Singh, J. S. Bhatia, and J. Kaur, “Eye tracking based driver fatigue monitoring and warning system”, in Proc. IEEE IICPE, New Delhi, India, Jan. 2014. 


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