DROWSINESS DETECTION AND PREVENTION USING ARTIFICIAL FEATURE RECOGNITION
In Portugal, in the year 2022, there were 32.788 reported road accidents. Among the identified causes, driver fatigue emerged as the second major contributor to these incidents. Efforts to address this issue have resulted in the development of legislation over the years. Concurrently, aligning with...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Ponteditora
2023-12-01
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| Series: | E3 |
| Subjects: | |
| Online Access: | https://www.revistas.ponteditora.org/index.php/e3/article/view/873 |
| _version_ | 1797234192963076096 |
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| author | Damiana Guedes Miguel Mota Daniel Azevedo Pedro Lopes Francisco Soares |
| author_facet | Damiana Guedes Miguel Mota Daniel Azevedo Pedro Lopes Francisco Soares |
| author_sort | Damiana Guedes |
| collection | DOAJ |
| description |
In Portugal, in the year 2022, there were 32.788 reported road accidents. Among the identified causes, driver fatigue emerged as the second major contributor to these incidents. Efforts to address this issue have resulted in the development of legislation over the years. Concurrently, aligning with initiatives in the European Union, the Fédération Internationale de l'Automobile (FIA) has actively promoted collaboration with the automotive industry to integrate safety-enhancing systems in vehicles, aiming to alleviate challenges such as driver fatigue.
The proposed strategy represents an intermediary solution, bridging the gap between the security provided by in-vehicle systems and the accessibility offered by mobile systems. The project's overarching goal is to ensure cost-effectiveness, efficiency, and widespread applicability. Leveraging Microsoft's Face API technology, the project seeks to capitalize on artificial intelligence to unlock a range of features, thereby achieving tasks that were previously deemed impractical or financially burdensome.
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| first_indexed | 2024-04-24T16:28:10Z |
| format | Article |
| id | doaj.art-6c2a68ff5678498ebdf59cffb3f0fd2e |
| institution | Directory Open Access Journal |
| issn | 2183-380X 2183-7201 |
| language | English |
| last_indexed | 2024-04-24T16:28:10Z |
| publishDate | 2023-12-01 |
| publisher | Ponteditora |
| record_format | Article |
| series | E3 |
| spelling | doaj.art-6c2a68ff5678498ebdf59cffb3f0fd2e2024-03-30T15:10:19ZengPonteditoraE32183-380X2183-72012023-12-019210.29073/e3.v9i2.873DROWSINESS DETECTION AND PREVENTION USING ARTIFICIAL FEATURE RECOGNITIONDamiana Guedes0Miguel Mota1Daniel Azevedo2Pedro Lopes3Francisco Soares4Instituto Politécnico de Viseu - ESTGLCERNAS-IPV Research Centre, Polytechnic Institute of ViseuCISeD - Research Centre in Digital Services, Polytechnic Institute of ViseuPolytechnic Institute of Viseu - School of Technology and Management of Lamego.Polytechnic Institute of Viseu - School of Technology and Management of Lamego. In Portugal, in the year 2022, there were 32.788 reported road accidents. Among the identified causes, driver fatigue emerged as the second major contributor to these incidents. Efforts to address this issue have resulted in the development of legislation over the years. Concurrently, aligning with initiatives in the European Union, the Fédération Internationale de l'Automobile (FIA) has actively promoted collaboration with the automotive industry to integrate safety-enhancing systems in vehicles, aiming to alleviate challenges such as driver fatigue. The proposed strategy represents an intermediary solution, bridging the gap between the security provided by in-vehicle systems and the accessibility offered by mobile systems. The project's overarching goal is to ensure cost-effectiveness, efficiency, and widespread applicability. Leveraging Microsoft's Face API technology, the project seeks to capitalize on artificial intelligence to unlock a range of features, thereby achieving tasks that were previously deemed impractical or financially burdensome. https://www.revistas.ponteditora.org/index.php/e3/article/view/873Artificial IntelligenceAlarmSecurityControlAPIIoT |
| spellingShingle | Damiana Guedes Miguel Mota Daniel Azevedo Pedro Lopes Francisco Soares DROWSINESS DETECTION AND PREVENTION USING ARTIFICIAL FEATURE RECOGNITION E3 Artificial Intelligence Alarm Security Control API IoT |
| title | DROWSINESS DETECTION AND PREVENTION USING ARTIFICIAL FEATURE RECOGNITION |
| title_full | DROWSINESS DETECTION AND PREVENTION USING ARTIFICIAL FEATURE RECOGNITION |
| title_fullStr | DROWSINESS DETECTION AND PREVENTION USING ARTIFICIAL FEATURE RECOGNITION |
| title_full_unstemmed | DROWSINESS DETECTION AND PREVENTION USING ARTIFICIAL FEATURE RECOGNITION |
| title_short | DROWSINESS DETECTION AND PREVENTION USING ARTIFICIAL FEATURE RECOGNITION |
| title_sort | drowsiness detection and prevention using artificial feature recognition |
| topic | Artificial Intelligence Alarm Security Control API IoT |
| url | https://www.revistas.ponteditora.org/index.php/e3/article/view/873 |
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