Detecting Driver Attentiveness Using OpenCV Machine learning is a cutting-edge real-time monitoring system that assesses a driver's level of attentiveness while driving in order to increase road safety. This research uses machine learning methods in conjunction with OpenCV-powered computer vision techniques to identify early signs of driver distraction and tiredness. The system determines if a motorist is fatigued or still focused on the road by continuously evaluating facial cues such head placement, eye movements, blink frequency, and yawning.

Live video input from an in-car camera is processed by the system, which distinguishes between alert and inattentive states using facial landmark detection. In order to help the driver restore focus, it detects indications of inattention or tiredness and sends out real-time alerts, including notifications or alarms. Through proactive detection of inattention and potential accident prevention, this research helps reduce human error-related road accidents, improving safety for pedestrians and drivers alike. It is especially advantageous for long-distance drivers, fleet management, and autonomous vehicle applications since it combines automated monitoring with AI-driven decision-making to provide a dependable and effective driver safety solution.