Real-Time Driver's Drowsiness Monitoring Based on Dynamically Varying Threshold

One of the most prevailing problems across the globe nowadays is the booming number of road accidents. Improper and inattentive driving is one of the major cause of road accidents. Driver's drowsiness or lack of concentration is considered as a dominant reason for such mishaps. Research in the field of driver drowsiness monitoring may help to reduce the accidents. This paper therefore proposes a non-intrusive approach for implementing a driver's drowsiness alert system which would detect and monitor the yawning and sleepiness of the driver. The system uses Histogram Oriented Gradient (HOG) feature descriptor for face detection and facial points recognition. Then SVM is used to check whether detected object is face or non-face. It further monitors the Eye Aspect Ratio (EAR) and Mouth Aspect Ratio (MAR) of the driver up to a fixed number of frames to check the sleepiness and yawning. Since the drowsiness or tiredness of the driver is also based on the number of hours he or she has been driving, an additional feature of varying the threshold frames for eyes and mouth is included. This makes the system more sensitive towards drowsiness detection. Also, this requires the inclusion of face recognition implementation so that monitoring can be done individually for every driver. Our experimental results shows that our proposed framework perform well.