Abstract
In this paper, we studied tiredness measurement based on several different detection methods in real time. We know that the driver tiredness is one of the major causes of traffic accidents. So tiredness detection can play a vital role for preventing road accidents. By developing an automatic solution for alerting drivers of tiredness before an accident occurs, this could reduce the number of traffic accidents. The Haar-cascade classifier is exploited based on Haar-like features to find the eyes. The main purpose of the Haar-cascade classifier is to classify closed or open state of the eyes. If we can notice that the eyes are closed for a predefined span of time, we consider the state of the eyes can be closed. Based on this closed-state of the eye, a notification (like alarm) is initiated to alert. We have detected only right eye for saving processing load on the system. The reason is that when a person closes his eyes he usually does not close one eye, but both eyes at the same time. Several steps are taken into account for this system; we first capture the frame from the webcam. Then we need to detect face as well as eye. To detect blinks, we process ROI (region of image) of pupil area. Our result is found to be satisfactory.
Highlights
Computer vision-based activity recognition, behavior understanding, gait analysis, emotion deciphering, fall detection assessment, eye-blink study, face recognition, etc. are major application areas for the last decade in different fields [3] [4] [7] [9] [12] [13] [16] [23] [24]
Eye blinking has a wide range of applications in human-computer interaction (HCI) like the eye typing applications as in [8], for disabled persons to operate all mouse operations, driving safety for general people like Google glass and so on
We have developed a real time application for the purpose of safetydriving
Summary
Computer vision-based activity recognition, behavior understanding, gait analysis, emotion deciphering, fall detection assessment, eye-blink study, face recognition, etc. are major application areas for the last decade in different fields [3] [4] [7] [9] [12] [13] [16] [23] [24]. Studies related to tiredness evaluation or eye-blinking rate becomes necessary in this arena for some important applications, especially in driver’s alert system from micro-sleep. Driver alert system is very much required if possible [1] [2] [5] [35] [36]. This field is very challenging due to multiple factors related to the position of the eyes, head movements, background lights and vicinity, distance, etc. In the field of active safety research, developing a system for assessing driver’s alertness level is becoming a central issue [1] [2] [5]. Eye blinking has a wide range of applications in human-computer interaction (HCI) like the eye typing applications as in [8], for disabled persons to operate all mouse operations, driving safety for general people like Google glass and so on
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