Real-Time Distracted Driver Detection and Monitoring using Computer Vision

Driver weariness takes a heavy toll in traffic fatalities, notably among truck drivers who work long hours, especially during night shifts, and bus drivers who travel long or nighttime routes. This issue endangers passengers worldwide. The proposed solution involves recording the driver with a camera and applying image processing techniques to detect his or her face in each frame. Specific facial landmarks are determined, and parameters such as nose length, mouth aperture are analysed in order to address this growing problem, this project aims to provide the state-of-the-art system It makes use of computer vision techniques and deep learning to continuously monitor and detect distractions in real time. The technology uses video feeds from in -cabin cameras to analyse driver's facial expressions, eye movements, and body position to detect crucial signs of distraction, such as phone use, drowsiness, or lack of aim on the road. Powerful facial and posture detection algorithms are used to effectively detect whether a driver is not paying attention or is involved in activities that affect their ability to drive safely. Adaptive thresholding is used to assess fatigue based on these indicators.