Optimized driver safety through driver fatigue detection methods

Abstract

Road accidents are prone to number human deaths. There are different reasons leads to road accidents, but drivers fatigue or distraction is main threat in major accidental cases. Therefore, recently various methods explained by many authors for early detection of driver drowsiness in order to prevent accidents on road. The methods are commonly known as driver face monitoring methods explained for driver fatigue detection also for the accident avoidance. Many of methods are based on image processing concepts in which images of driver face are captured, and then extracted the eyes, mouth & head position of the driver from input image. The symptoms for detecting the drowsiness are eyelid distance, eyelid closure over time, yawing, head orientation, head nodding, yawning etc. Some methods are based on driver's performance also. In this paper, we are presenting the detailed study on different approaches for driver safety using various driver drowsiness or distraction detection techniques. After discussing the limitations of previous method, we designed the novel driver safety method with objective of improving the efficiency of driver distraction detection. The designed method is based on optimized face detector and feature extraction method with SVM classifier. The outcome of this paper is new efficient driver safety technique based on video image processing.