Real-time face and eye tracking using OpenCV/Deep Learning. Drowsiness detection through eye aspect ratio (EAR) and blink frequency. Instant audio/visual alerts when signs of fatigue are identified. Flexible deployment on PC or edge devices. Potential integration with IoT for vehicle safety systems.

Real-time drowsiness detection using YOLOv5 – detects "awake" and "drowsy" states from webcam input with bounding boxes and confidence scores.