Embedded Real Time Blink Detection System for Driver Fatigue Monitoring

Fatigue induced vehicle accidents have seen an increase in the last few decades. Fatigue monitoring using noninvasive and real time image processing and computer vision techniques have shown great promise and are an active research area. To that extent, in the proposed work a blink detection algorithm is proposed that serves as a visual cue that may be correlated to the state of fatigue of the driver. Using a complimentary but independent approach, shape analysis and histogram analysis are carried out in parallel to perform the blink detection task. Close to real time performance and a high level of accuracy in controlled settings show great promise of such approach in enhancing the monitoring of the driver’s blinking patterns. One of the main constraints of using such algorithm in a real world setting is the minimized processing time required to allow for sufficient driver response time. In this work implementation of the algorithm is described using optimization techniques to meet such latency requirements. The validation of the algorithm was carried out by visual inspection of the video sequences in terms of precision and accuracy. The presented blink detection algorithm has a precision rate of 84% and an accuracy rate of 69% obtained through using 12 sequences of different duration videos in varying lighting conditions using a small sample of participants.