Methodology for in-the-Wild Driver Monitoring Dataset Formation
Driver distraction and fatigue have become one of the leading causes of severe traffic accidents. Hence, the systems that implement driver monitoring systems are crucial. Usually such systems used a monocular camera to recognize driver behavior. Even with the growing development of advanced driver a...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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FRUCT
2022-04-01
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| Series: | Proceedings of the XXth Conference of Open Innovations Association FRUCT |
| Subjects: | |
| Online Access: | https://www.fruct.org/publications/fruct31/files/Bul.pdf |
| Summary: | Driver distraction and fatigue have become one of the leading causes of severe traffic accidents. Hence, the systems that implement driver monitoring systems are crucial. Usually such systems used a monocular camera to recognize driver behavior. Even with the growing development of advanced driver assistance systems and the introduction of third-level autonomous vehicles, this task is still trending and complex due to challenges such as in-cabin illumination change and the dynamic background. To reliably compare and validate driver inattention monitoring methods a limited number of public datasets are available. The paper proposes a methodology for in-the-wild dataset creation of vehicle driver for recording an oculomotor activity, a video images of a driver as well as relevant smartphone sensors that track vehicle movement. Based on the methodology we plan to conduct in-the-wild experiments. |
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| ISSN: | 2305-7254 2343-0737 |