Detecting drowsiness while driving by measuring eye movement - a pilot study

Hayami, Takehito; Matsunaga, Kazuhide; Shidoji, Kazunori; Matsuki, Yuji

doi:10.1109/itsc.2002.1041206

Cited by 44 publications

(16 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…According to the frequency and length of eye closure or blinking, [16] facial image detection can determine driver attentiveness. [17,18] These image-detection technologies can also be applied as learning detection systems to assess learner status. Due to different environments and limitations, the objective behaviors to detect differ; thus, what must evaluate student status and degree of attentiveness.…”

Section: Related Workmentioning

confidence: 99%

Attentiveness Assessment in Learning Based on Fuzzy Logic Analysis

Hwang

Yang

2008

2008 Eighth International Conference on Intelligent Systems Design and Applications

View full text Add to dashboard Cite

Learner attention affects learning efficiency. However, in many classes, teachers cannot assess the degree of attention of every student. When a teacher is capable of addressing inattentive students immediately, he can avoid situations in which students are inattentive. Many studies have analyzed student attentiveness by the applying of Image Detection Technologies. If this mechanism can be applied to inclass learning, it will help teachers keep students attentive, and reduce teacher load during class. This study mainly applies Fuzzy Logic analysis of student facial images when participating in class. Applying Fuzzy Logic can prevent erroneous judgments associated with a single term, and help teachers deal with student attentiveness

show abstract

Section: Related Workmentioning

confidence: 99%

Attentiveness Assessment in Learning Based on Fuzzy Logic Analysis

Hwang

Yang

2008

2008 Eighth International Conference on Intelligent Systems Design and Applications

View full text Add to dashboard Cite

show abstract

“…2 How to locate the eyes of a driver under extreme lighting condition is an important issue for a successful intelligent transportation system. Detecting the eye line can help locate eyes.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Horizontal Eye Line Detection Under Various Environments

Jing

Chen

2010

Int. J. Patt. Recogn. Artif. Intell.

View full text Add to dashboard Cite

The eye line is de¯ned to be a horizontal line passing the two eyes of a human face. It can be used to help locate the true positions of eyes and face. In this paper, we propose a method to extract the horizontal eye line of a face under various environments. Based on the facts that the eye color is very di®erent from skin color and the gray level variance of an eye is high, some eye-like regions are¯rst located. Then the horizontal eye line is extracted based on the located eye-like regions and some geometric properties in a face. Experimental results are given to show that the proposed method is robust under a wide range of lighting conditions, di®erent poses and races. The detection rate for HHI face database is 95.63%. For Champion face database, the detection rate is 94.27%.

show abstract

“…Hayami et al used eye's status to adjudge the drivers' behavior to distinguish whether they are drowsiness or not [4]. In [4], a few parameters are roughly calculated so the precision is not good enough to provide the safety. These methods only solve the problems that the drivers fall in asleep.…”

Section: Introductionmentioning

confidence: 99%

A real-time vision-based safety assist system

Chen

Peng

et al. 2008

2008 IEEE International Conference on Systems, Man and Cybernetics

View full text Add to dashboard Cite

In this paper, a real-time vision-based safety assist system is proposed to provide the driver an assistance system improving the security on driving and after parking. The estimated distances and a friendly intuitive graph are shown on the screen for drivers to examine the distance between other vehicles. The driving status can also be recorded in H.264. Furthermore, the users' mobile phones can monitor the images inside the vehicles to prevent the thieves after parking. The system has been implemented with the embedded systems and tested by the real road environment.

show abstract

Detecting drowsiness while driving by measuring eye movement - a pilot study

Cited by 44 publications

References 9 publications

Attentiveness Assessment in Learning Based on Fuzzy Logic Analysis

Attentiveness Assessment in Learning Based on Fuzzy Logic Analysis

A Novel Method for Horizontal Eye Line Detection Under Various Environments

A real-time vision-based safety assist system

Contact Info

Product

Resources

About