http://www.cnr.it/ontology/cnr/individuo/prodotto/ID52562
A visual approach for driver inattention detection (Articolo in rivista)
- Type
- Label
- A visual approach for driver inattention detection (Articolo in rivista) (literal)
- Anno
- 2007-01-01T00:00:00+01:00 (literal)
- Alternative label
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Tiziana D'Orazio; Marco Leo; Cataldo Guaragnella; Arcangelo Distante (literal)
- Pagina inizio
- Pagina fine
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
- Rivista
- Note
- ISI Web of Science (WOS) (literal)
- Scopu (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- ISSIA-CNR, DEE-Politecnico di Bari (literal)
- Titolo
- A visual approach for driver inattention detection (literal)
- Abstract
- Monitoring driver fatigue, inattention, and lack of sleep is very
important in preventing motor vehicles accidents. A visual system
for automatic driver vigilance has to address two fundamental
problems. First of all it has to analyze the sequence of images
and detect if the driver has his eyes open or closed, and then it
has to evaluate the temporal occurrence of eyes open to estimate
the driver's visual attention level. In this paper we propose a
visual approach that solves both problems. A neural classifier is
applied to recognize the eyes in the image, selecting two
candidate regions that might contain the eyes by using iris
geometrical information and symmetry. The novelty of this work is
that the algorithm works on complex images without constraints on
the background, skin color segmentation and so on. Several
experiments were carried out on images of subjects with different
eye colors, some of them wearing glasses, in different light
conditions. Tests show robustness with respect to situations such
as eyes partially occluded, head rotation and so on. In
particular, when applied to images where people have eyes closed
the proposed algorithm correctly reveals the absence of eyes.
Next, the analysis of the eye occurrence in image sequences is
carried out with a probabilistic model to recognize anomalous
behaviors such as driver inattention or sleepiness. Image
sequences acquired in the laboratory and while people were driving
a car were used to test the driver behavior analysis and
demonstrate the effectiveness of the whole approach. (literal)
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