Name
Title | ||
|---|---|---|
Driver drowsiness detection: a comparison between intrusive and non-intrusive signal acquisition methods |
Abstract | ||
|---|---|---|
Driver drowsiness is a major cause of road accidents, many of which result in fatalities. A solution to this problem is the inclusion of a drowsiness detector in vehicles to alert the driver if sleepiness is detected. To detect drowsiness, physiologic, behavioral (visual) and vehicle-based methods can be used, however, only measures that can be acquired non-intrusively are viable in a real life application. This work uses data from a real-road experiment with sleep deprived drivers to compare the performance of driver drowsiness detection using intrusive acquisition methods, namely electrooculogram (EOG), with camera-based, non-intrusive, methods. A hybrid strategy, combining the described methods with electrocardiogram (ECG) measures, is also evaluated. Overall, the obtained results show that drowsiness detection performance is similar using non-intrusive camera-based measures or intrusive EOG measures. The detection performance increases when combining two methods (ECG + visual) or (ECG + EOG). |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/EUVIP.2018.8611704 | 2018 7th European Workshop on Visual Information Processing (EUVIP) |
Keywords | Field | DocType |
Driver drowsiness,Camera-based methods,ECG,EOG | Computer vision,Signal acquisition,Computer science,Feature extraction,Electrooculography,Artificial intelligence,Detector | Conference |
ISSN | ISBN | Citations |
2164-974X | 978-1-5386-6898-6 | 0 |
PageRank | References | Authors |
0.34 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Licínio Oliveira | 1 | 14 | 1.33 |
| Jaime S. Cardoso | 2 | 543 | 68.74 |
| André Lourenço | 3 | 312 | 45.33 |
| Christer Ahlstrom | 4 | 51 | 6.46 |