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Driver drowsiness remains a significant cause of road accidents worldwide.To address this issue, we propose a comprehensive Driver Drowsiness Detection System (DDDS) that leverages computer vision techniques, machine learning algorithms, and real-time monitoring to detect signs of drowsiness in drivers.Our system utilizes OpenCV for face detection, dlib for facial landmark detection, and various other libraries to identify indicators such as eye closure and yawning.Additionally, we have developed an intuitive interface using Flask framework, enabling seamless integration between the front end and the Python-based detection algorithms.Moreover, our system incorporates a historical tracking feature, allowing for the analysis of driver performance over time.
Avadhut et al. (Sat,) studied this question.