This paper presents a real-time driver drowsiness detection system for driving safety. Based on computer vision techniques , the driver’s face is located from a color video captured in a car. Then, face detection is employed to locate the regions of the driver’s eyes, which are used as the templates for eye tracking in subsequent frames. Finally, the tracked eye’s images are used for drowsiness detection in order to generate warning alarms. The proposed approach has three phases: Face, Eye detection and drowsiness detection. The role of image processing is to recognize the face of the driver and then extracts the image of the eyes of the driver for detection of drowsiness. The Haar face detection algorithm takes captured frames of image as input and then the detected face as output. Next, CHT is used to tracking eyes from the detected face. If the eyes are closed for a predefined period of time the eyes of the driver will be considered closed and hence an alarm will be started to alert the driver. The proposed system was tested on a Raspberry pi 3 Model B with 1GB RAM with use of Logitech HD Webcam C270. The experimental results appears quite encouraging and promising. The system could reach more than 15 frames per second for face and eye tracking, and the average correct rate for eye location and tracking could achieve 99.0% on some test videos. Thus, it can be concluded that the proposed approach is a low cost and effective solution method for a real-time of driver drowsiness detection.
Prof. Dr. Bilal BİLGİN