DRIVER DROWSINESS DETECTION USING IMAGE PROCESSING
Drowsy driving has played a significant role in a number of traffic incidents throughout the years. Implementing a system with an alarm output to inform tired drivers to focus on the road can help prevent car accidents and other undesired situations. As one strategy to minimize accidents, save money, and reduce losses and sufferings, an intelligent system is being developed to detect driver drowsiness and trigger an alarm to notify drivers. However, present approaches have several drawbacks due to the considerable fluctuation of surrounding conditions. Bad lighting can make it difficult for the camera to precisely measure the driver's face and eye. Due to late detection or no detection, this will have an impact on image processing analysis. Reduce the technique's precision and efficiency. Several strategies have been explored and analyses in order to determine the best technique for detecting driver tiredness with the highest accuracy. In this paper, we propose a real-time system that uses a computerized camera to follow and process the driver's eye using Python, d lip, and Open CV. The driver's eye region is continuously measured and calculated to assess drowsiness before an output alarm is triggered to alert the driver.
Drowsiness detection, face detection and tracking, eye detection and tracking, eye aspect ratio, yawning, eye closure, computer vision, real time detection.