Analysis of Driver Drowsiness Detection System Based on Landmarks and MediaPipe

Driver drowsiness is one of the leading causes of traffic accidents, especially during long-distance journeys. This study developed a detection system based on landmarks and the MediaPipe framework to analyze drowsiness through eye blink duration. The system employs coordinate point initialization using regression trees to accurately detect objects, such as eyes. The research data consists of 30 videos, each lasting 30 seconds, collected from four Trans Java bus drivers. The videos were extracted to identify facial detection histograms and analyzed based on eye blink duration. The testing results showed a detection accuracy of 81% with an error rate of 19% for distances of 10 to 100 cm, while testing with 30 videos achieved an average accuracy of 88.745% and a Mean Squared Error (MSE) of 7.615%. The test results show that CNN outperforms MediaPipe in detecting drowsiness, with a higher average accuracy of 76.79% compared to 73.83% and a lower MSE value of 47.33 compared to 48.27. CNN is also more consistent in handling extreme lighting variations, while MediaPipe excels in processing efficiency, making it suitable for devices with limited resources. This study demonstrates that the landmarks and MediaPipe-based system effectively and innovatively detects drowsiness, offering a solution to improve driver safety during trips.