• M,Kumar Raju
• M.Naga Keerthi

Driver drowsiness poses a serious threat to road safety, necessitating effective detection systems. This project employs computer vision techniques to develop a real-time driver drowsiness detection system. Using a camera inside the vehicle, facial features and eye movements are monitored continuously. Key indicators such as eye closure duration and head position changes are analyzed to determine the driver's state. A convolutional neural network (CNN) is trained to classify between alert and drowsy states based on these features. The system operates in real-time, providing immediate alerts when drowsiness is detected. Extensive testing under varied driving conditions and lighting scenarios validates the system's efficacy and reliability. Results show promising accuracy in detecting drowsiness, thereby enhancing road safety by alerting drivers promptly. This research contributes to advancing driver safety technologies through the integration of computer vision and machine learning. Future enhancements aim to optimize performance across diverse driving environments. Overall, this project underscores the potential of computer vision in mitigating the risks associated with driver drowsiness, ensuring safer road experiences globally.

Driver drowsiness detection, computer vision, real-time monitoring, facial feature analysis, eye movement analysis, convolutional neural networks (CNNs), road safety, machine learning, alert system, vehicle safety, image processing, driver monitoring systems, eye closure detection, head position tracking, driver state classification, road accidents prevention.

"Computer-Vision-Project-Driver-drowsiness-detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 7, page no.f563-f571, July-2024, Available :http://www.jetir.org/papers/JETIR2407574.pdf

"Computer-Vision-Project-Driver-drowsiness-detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 7, page no. ppf563-f571, July-2024, Available at : http://www.jetir.org/papers/JETIR2407574.pdf