• Chaithra B
• Rekha D
• Deeksha N
• Ashritha S
• Ms Poojitha

--Driver distraction is a major contributor to road accidents, particularly due to mobile phone use and fatigue. Ensuring driver attentiveness is therefore critical for road safety. This project presents a Real-Time Driver Distraction Detection and Alert System that uses computer vision to monitor facial expressions, eye movements, blinking patterns, and head orientation from a live camera feed. The system identifies signs of drowsiness, phone usage, or looking away from the road and provides instant audio-visual alerts to help the driver regain focus and prevent accidents. Designed to be low-cost and non-intrusive, the system can be easily installed in existing vehicles without significant modifications. It also supports the development of intelligent transportation systems, with potential future enhancements including IoT integration, adaptive alert mechanisms, and partial automation to further improve road safety.

Yolo8; Mediapipe; Heuristics; Pygame; Driver monitoring; Driver distraction; Real-time detection; YOLOv8; MediaPipe; Heuristic analysis; Eye aspect ratio; Head pose estimation; Alert system; Vehicle safety

"Real Time Driver Distraction Detection With Alarm System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 1, page no.a505-a514, January-2026, Available :http://www.jetir.org/papers/JETIR2601062.pdf

"Real Time Driver Distraction Detection With Alarm System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 1, page no. ppa505-a514, January-2026, Available at : http://www.jetir.org/papers/JETIR2601062.pdf