The Drowsiness Detection prototype is a groundbreaking project designed to enhance road safety by detecting signs of driver drowsiness in real-time. Leveraging cutting-edge technologies, the system monitors facial features using computer vision and machine learning algorithms to issue timely alerts, mitigating the risks associated with drowsy driving.
Facial Landmark Detection: The system uses the dlib library to analyze key facial landmarks, focusing on areas such as eyes and mouth, to identify signs of drowsiness.
Advanced Algorithms: Utilizing eye and mouth aspect ratios, the system calculates thresholds for drowsiness detection, ensuring sensitivity and specificity. Yawn detection is also integrated for a comprehensive approach.
Real-time Operation: The prototype operates in real-time, continuously processing frames using OpenCV, dlib, and imutils libraries. It ensures constant vigilance to changes in the driver's facial features.
Data Collection and Analysis: Facial images are captured during various driving conditions, and a pre-trained model is employed to map facial points. Extensive data analysis involves ROC curve plotting and the application of the Otsu method for image segmentation.
Alert Mechanism: Alerts are triggered based on predefined thresholds for Eye and Mouth Aspect Ratios, sustained drowsy frames, and identification of yawning events. The alert system includes visual and auditory cues, enhancing communication between the system and the driver.
Proactive Measures: In cases of prolonged drowsiness, the system initiates a web browser search for nearby hotels or motels, providing automated break assistance to prioritize driver well-being.
The Drowsiness Detection prototype offers a comprehensive solution to promote road safety through proactive monitoring and timely alerts. With its integration of advanced technologies and thoughtful alert mechanisms, the system contributes to a more intuitive and responsive driving assistance system, prioritizing driver well-being and enhancing overall road safety.
shape_predictor_68_face_landmarks.dat