Name
Abstract | ||
|---|---|---|
The main objective of this paper is to determine the feasibility of designing a driver drunkenness detection system based on the dynamic analysis of a subject’s pupillary light reflex (PLR). This involuntary reaction is widely utilized in the medical field to diagnose a variety of diseases, and in this paper, the effectiveness of such a method to reveal an impairment condition due to alcohol abuse is evaluated. The test method consists in applying a light stimulus to one eye of the subject and to capture the dynamics of constriction of both eyes; for extracting the pupil size profiles from the video sequences, a two-step methodology is described, where in the first phase, the iris/pupil search within the image is performed, and in the second stage, the image is cropped to perform pupil detection on a smaller image to improve time efficiency. The undesired pupil dynamics arising in the PLR are defined and evaluated; a spontaneous oscillation of the pupil diameter is observed in the range [0, 2] Hz and the accommodation reflex causes pupil constriction of about 10% of the iris diameter. A database of pupillary light responses is acquired on different subjects in baseline condition and after alcohol consumption, and for each one, a first-order model is identified. A set of features is introduced to compare the two populations of responses and is used to design a support vector machine classifier to discriminate between “Sober” and “Drunk” states. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/tits.2018.2871262 | IEEE Transactions on Intelligent Transportation Systems |
Keywords | Field | DocType |
Vehicles,Fatigue,Video sequences,Sleep,Roads,Monitoring,Cameras | Computer vision,Pupillary light reflex,Support vector machine classifier,Pupil,Pupil diameter,Artificial intelligence,Stimulus (physiology),Engineering,Pupil constriction,Accommodation reflex | Journal |
Volume | Issue | ISSN |
20 | 8 | 1524-9050 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alessandro Amodio | 1 | 0 | 1.69 |
| Michele Ermidoro | 2 | 0 | 1.69 |
| Davide Maggi | 3 | 0 | 0.34 |
| Simone Formentin | 4 | 127 | 29.41 |
| Sergio M. Savaresi | 5 | 943 | 142.05 |