A major contributing element to road safety is driver fatigue, which raises the possibility of collisions dramatically. These hazards may be reduced by early diagnosis of sleepiness, however conventional techniques often fall short in the short term. This work uses behavioral analysis with OpenCV, a potent computer vision toolkit, to provide a unique method of predicting driver sleepiness. The suggested system makes use of OpenCV to examine a variety of driver behavioral traits, including head position, blink frequency, and eye movements. Through the use of a camera mounted inside the car to record live video, the system analyzes visual inputs to detect indicators of fatigue. Sophisticated image processing methods are used to monitor and evaluate facial expressions that are suggestive of sleepiness, such as eyelid movement and gaze direction. The research improves the forecast accuracy of tiredness by integrating OpenCV with machine learning methods. To identify patterns linked to tiredness, these algorithms are trained using a collection of driving photos that have been annotated. The system's capacity to analyze data in real-time guarantees that drivers get notifications in a timely manner, encouraging prompt remedial action and lowering the risk of collisions. According to preliminary test findings, the technology can accurately and consistently identify when a driver would get sleepy. By offering a dependable, real-time monitoring tool, the method provides a workable way to increase driver safety. In order to improve detection capabilities, future work will concentrate on growing the dataset, improving the algorithm's accuracy, and investigating new behavioral characteristics. The present research underscores the potential of OpenCV in the development of sophisticated driver assistance systems and stresses the significance of combining computer vision technology with behavioral analysis to improve road safety.

A major contributing element to road safety is driver fatigue, which raises the possibility of collisions dramatically.