Driver Drowsiness Detection and Alert System Using Computer Vision - Vinutha, K.; Ashwini, N.; Raj, Amrit; Sukruth, Jayam; Praneeth, M.; Anand, Shubham (New Approaches in Engineering Research Vol. 1, 2021)
In recent years, an increase in the demand for modern transportation necessitates a faster car-parc growth. Driver drowsiness has been generally recognized as a significant factor in the increasing number of road accidents. OpenCV technology employ the movement of the driver’s eyes and position of the driver’s head to determine the level of their fatigue. Using image capture, the photo of the driver can be taken by means of a web camera mounted in the car. Given the way a video is made by the camera, the measurement has to be done to either side of the video stream to get the edges for the further operation. By partitioning the video into individual frames, the situation in which the video is recorded is determined. The video will subsequently be separated into edges for exploration purposes. The region that contains the image of the driver is acknowledged at this point. For every package, a predefined count is for face region. By face recognition, we infer that through a certain improvement in the use of the PC, we consider the crucial facial characteristics. The eyes of the face should be identified upon and classified for further focus. The most important decision parameter to assess the driver's state is in the eyes.