Real-time driver monitoring system with facial landmark-based eye closure detection and head pose recognition
Abstract This paper introduces a real-time Driver Monitoring System (DMS) designed to monitor driver behavior while driving, employing facial landmark estimation-based behavior recognition. The system utilizes an infrared (IR) camera to capture and analyze video data. Through facial landmark estimat...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-10-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-44955-1 |
| _version_ | 1797452983187800064 |
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| author | Dohun Kim Hyukjin Park Tonghyun Kim Wonjong Kim Joonki Paik |
| author_facet | Dohun Kim Hyukjin Park Tonghyun Kim Wonjong Kim Joonki Paik |
| author_sort | Dohun Kim |
| collection | DOAJ |
| description | Abstract This paper introduces a real-time Driver Monitoring System (DMS) designed to monitor driver behavior while driving, employing facial landmark estimation-based behavior recognition. The system utilizes an infrared (IR) camera to capture and analyze video data. Through facial landmark estimation, crucial information about the driver’s head posture and eye area is extracted from the detected facial region, obtained via face detection. The proposed method consists of two distinct modules, each focused on recognizing specific behaviors. The first module employs head pose analysis to detect instances of inattention. By monitoring the driver’s head movements along the horizontal and vertical axes, this module assesses the driver’s attention level. The second module implements an eye-closure recognition filter to identify instances of drowsiness. Depending on the continuity of eye closures, the system categorizes them as either occasional drowsiness or sustained drowsiness. The advantages of the proposed method lie in its efficiency and real-time capabilities, as it solely relies on IR camera video for computation and analysis. To assess its performance, the system underwent evaluation using IR-Datasets, demonstrating its effectiveness in monitoring and recognizing driver behavior accurately. The presented real-time Driver Monitoring System with facial landmark-based behavior recognition offers a practical and robust approach to enhance driver safety and alertness during their journeys. |
| first_indexed | 2024-03-09T15:16:18Z |
| format | Article |
| id | doaj.art-200cc921ef554a17b59adaf641de3a6f |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-09T15:16:18Z |
| publishDate | 2023-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-200cc921ef554a17b59adaf641de3a6f2023-11-26T13:02:39ZengNature PortfolioScientific Reports2045-23222023-10-0113111410.1038/s41598-023-44955-1Real-time driver monitoring system with facial landmark-based eye closure detection and head pose recognitionDohun Kim0Hyukjin Park1Tonghyun Kim2Wonjong Kim3Joonki Paik4Electronics and Telecommunications Research InstituteTQS KoreaCANLABElectronics and Telecommunications Research InstituteDepartment of Image, Chung-Ang UniversityAbstract This paper introduces a real-time Driver Monitoring System (DMS) designed to monitor driver behavior while driving, employing facial landmark estimation-based behavior recognition. The system utilizes an infrared (IR) camera to capture and analyze video data. Through facial landmark estimation, crucial information about the driver’s head posture and eye area is extracted from the detected facial region, obtained via face detection. The proposed method consists of two distinct modules, each focused on recognizing specific behaviors. The first module employs head pose analysis to detect instances of inattention. By monitoring the driver’s head movements along the horizontal and vertical axes, this module assesses the driver’s attention level. The second module implements an eye-closure recognition filter to identify instances of drowsiness. Depending on the continuity of eye closures, the system categorizes them as either occasional drowsiness or sustained drowsiness. The advantages of the proposed method lie in its efficiency and real-time capabilities, as it solely relies on IR camera video for computation and analysis. To assess its performance, the system underwent evaluation using IR-Datasets, demonstrating its effectiveness in monitoring and recognizing driver behavior accurately. The presented real-time Driver Monitoring System with facial landmark-based behavior recognition offers a practical and robust approach to enhance driver safety and alertness during their journeys.https://doi.org/10.1038/s41598-023-44955-1 |
| spellingShingle | Dohun Kim Hyukjin Park Tonghyun Kim Wonjong Kim Joonki Paik Real-time driver monitoring system with facial landmark-based eye closure detection and head pose recognition Scientific Reports |
| title | Real-time driver monitoring system with facial landmark-based eye closure detection and head pose recognition |
| title_full | Real-time driver monitoring system with facial landmark-based eye closure detection and head pose recognition |
| title_fullStr | Real-time driver monitoring system with facial landmark-based eye closure detection and head pose recognition |
| title_full_unstemmed | Real-time driver monitoring system with facial landmark-based eye closure detection and head pose recognition |
| title_short | Real-time driver monitoring system with facial landmark-based eye closure detection and head pose recognition |
| title_sort | real time driver monitoring system with facial landmark based eye closure detection and head pose recognition |
| url | https://doi.org/10.1038/s41598-023-44955-1 |
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