Dynamic set point model for driver alert state using digital image processing.
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| Title: | Dynamic set point model for driver alert state using digital image processing. |
|---|---|
| Authors: | Isaza, Cesar1 (AUTHOR) cesar.isaza@upq.mx, Anaya, Karina1 (AUTHOR), Fuentes-Silva, Carlos1 (AUTHOR), de Paz, Jonny Paul Zavala1 (AUTHOR), Rizzo, Amilcar1 (AUTHOR), Garcia-Moreno, Angel-Ivan1 (AUTHOR) |
| Source: | Multimedia Tools & Applications. Jul2019, Vol. 78 Issue 14, p19543-19563. 21p. |
| Subjects: | Feature extraction, Fatigue (Physiology), Behavioral assessment, Point set theory, Traffic accidents, Digital image processing |
| Abstract: | The driver fatigue and lose of attention while driving are the most important causes of traffic accidents. Each year more than one million of deaths occur due to these facts. Thus, this problem has been converted into a serious social issue with high impact not only in economic terms, but also in the public health sector all around the world. Several approaches based on computer vision systems have been proposed to deal with this severe situation, but none of them have fully considered the non-fatigue state as a primary knowledge to detect an unusual event of a person while driving. In fact, typical approaches to deal with the problem of fatigue detection, are based on the analysis of behavioral features extracted with digital image processing such as frequency of blinking, yawning, among others. However, the huge limitation is the short interval of time between each analysis, that generally is few frames per second. Furthermore, all available methods are focus in modeling the fatigue, instead of representing the set point alert state of the driver, which is the main core of the proposed strategy. Hence, in this paper a dynamic set point model for alert state while driving using digital image processing and machine learning techniques is presented. The approach uses an embedded system build with a Raspberry prototyping board and a USB HD camera. Raspbian operative system controls OPEN CV libraries written in Python to detect face parts with an algorithm running Harr descriptors. The features extracted were the position and orientation of the head throw several minutes. Then, a mixture of Gaussians model with its learning and updating stages is used to represent the behaviour of features. Also, a dataset was built considering professional and non-professional drivers under two main scenarios: real and simulated conditions. Experimental results show the viability of the method for posterior analysis of unusual events while driving like fatigue detection, cellphone call or chat detection, or any other distraction not related to the driving process. [ABSTRACT FROM AUTHOR] |
| Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 137453621 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Dynamic set point model for driver alert state using digital image processing. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Isaza%2C+Cesar%22">Isaza, Cesar</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> cesar.isaza@upq.mx</i><br /><searchLink fieldCode="AR" term="%22Anaya%2C+Karina%22">Anaya, Karina</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fuentes-Silva%2C+Carlos%22">Fuentes-Silva, Carlos</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22de+Paz%2C+Jonny+Paul+Zavala%22">de Paz, Jonny Paul Zavala</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rizzo%2C+Amilcar%22">Rizzo, Amilcar</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Garcia-Moreno%2C+Angel-Ivan%22">Garcia-Moreno, Angel-Ivan</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Multimedia+Tools+%26+Applications%22">Multimedia Tools & Applications</searchLink>. Jul2019, Vol. 78 Issue 14, p19543-19563. 21p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Fatigue+%28Physiology%29%22">Fatigue (Physiology)</searchLink><br /><searchLink fieldCode="DE" term="%22Behavioral+assessment%22">Behavioral assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Point+set+theory%22">Point set theory</searchLink><br /><searchLink fieldCode="DE" term="%22Traffic+accidents%22">Traffic accidents</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+image+processing%22">Digital image processing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The driver fatigue and lose of attention while driving are the most important causes of traffic accidents. Each year more than one million of deaths occur due to these facts. Thus, this problem has been converted into a serious social issue with high impact not only in economic terms, but also in the public health sector all around the world. Several approaches based on computer vision systems have been proposed to deal with this severe situation, but none of them have fully considered the non-fatigue state as a primary knowledge to detect an unusual event of a person while driving. In fact, typical approaches to deal with the problem of fatigue detection, are based on the analysis of behavioral features extracted with digital image processing such as frequency of blinking, yawning, among others. However, the huge limitation is the short interval of time between each analysis, that generally is few frames per second. Furthermore, all available methods are focus in modeling the fatigue, instead of representing the set point alert state of the driver, which is the main core of the proposed strategy. Hence, in this paper a dynamic set point model for alert state while driving using digital image processing and machine learning techniques is presented. The approach uses an embedded system build with a Raspberry prototyping board and a USB HD camera. Raspbian operative system controls OPEN CV libraries written in Python to detect face parts with an algorithm running Harr descriptors. The features extracted were the position and orientation of the head throw several minutes. Then, a mixture of Gaussians model with its learning and updating stages is used to represent the behaviour of features. Also, a dataset was built considering professional and non-professional drivers under two main scenarios: real and simulated conditions. Experimental results show the viability of the method for posterior analysis of unusual events while driving like fatigue detection, cellphone call or chat detection, or any other distraction not related to the driving process. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11042-019-7218-z Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 19543 Subjects: – SubjectFull: Feature extraction Type: general – SubjectFull: Fatigue (Physiology) Type: general – SubjectFull: Behavioral assessment Type: general – SubjectFull: Point set theory Type: general – SubjectFull: Traffic accidents Type: general – SubjectFull: Digital image processing Type: general Titles: – TitleFull: Dynamic set point model for driver alert state using digital image processing. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Isaza, Cesar – PersonEntity: Name: NameFull: Anaya, Karina – PersonEntity: Name: NameFull: Fuentes-Silva, Carlos – PersonEntity: Name: NameFull: de Paz, Jonny Paul Zavala – PersonEntity: Name: NameFull: Rizzo, Amilcar – PersonEntity: Name: NameFull: Garcia-Moreno, Angel-Ivan IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 07 Text: Jul2019 Type: published Y: 2019 Identifiers: – Type: issn-print Value: 13807501 Numbering: – Type: volume Value: 78 – Type: issue Value: 14 Titles: – TitleFull: Multimedia Tools & Applications Type: main |
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