Driver fatigue detection based on eye state

Abstract

Abstract. BACKGROUND: Nowadays, more and more traffic accidents occur because of driver fatigue. OBJECTIVE: In order to reduce and prevent it, in this study, a calculation method using PERCLOS (percentage of eye closure time) parameter characteristics based on machine vision was developed. It determined whether a driver's eyes were in a fatigue state according to the PERCLOS value. METHODS: The overall workflow solutions included face detection and tracking, detection and location of the human eye, human eye tracking, eye state recognition, and driver fatigue testing. The key aspects of the detection system incorporated the detection and location of human eyes and driver fatigue testing. The simplified method of measuring the PERCLOS value of the driver was to calculate the ratio of the eyes being open and closed with the total number of frames for a given period. RESULTS: If the eyes were closed more than the set threshold in the total number of frames, the system would alert the driver. CONCLUSION: Through many experiments, it was shown that besides the simple detection algorithm, the rapid computing speed, and the high detection and recognition accuracies of the system, the system was demonstrated to be in accord with the real-time requirements of a driver fatigue detection system.

Real‐time eye tracking for the assessment of driver fatigue

“…For example, LV proposed an image processing method for fatigue recognition based on adaptive locality preserving projections and obtained a classification accuracy of 93.8% by FKNN of using adaptive neighbourhood selection strategy [ 23 ]. Lin studied a calculation method for driver fatigue detection based on machine vision to calculate the value of PERCLOS to determine the eye closure proportion in a given time and achieved the average precision of 84% by the adaboost cascade classifier [ 24 ]. Choi developed a gaze zone detection system of using deep learning techniques for recognising driver's gaze zone and the correct rate reached to 95% in average [ 25 ].…”

Performance analysis in elite football: all in the game?

“…Recently, face recognition has been used to determine emotions in sports like tennis (Kovalchik and Reid 2018). Furthermore, driver fatigue was detected based on eye state (Lin et al 2015). These developments will likely find their way into football.…”

A Portable Fuzzy Driver Drowsiness Estimation System

“…With this objective, markers for the right eye (38,39,41,42), left eye (44,45,47,48), and mouth (62–64,66–68) are tracked for computing the distance between eyelids and lips. Eyes are considered closed when the space between eyelids is less than 20% of the open eye area (as defined in [ 39 ]), and the mouth is considered opened when its aspect ratio ( ) is greater than 0.7, as defined in [ 10 ].…”

“…To ensure accurate performance, the most critical component is the hardware that processes the images and generates the alarm. Due to the high computing power demanded by computer vision algorithms, most of the portable drowsiness detection systems reported in the literature [ 5 , 8 , 9 , 10 , 11 , 12 ] are computer-based, employing a laptop for data processing. As a result, the cost of these systems is elevated, and their application is limited to specific scenarios, not being adaptable to different vehicle conditions.…”