Using Machine Learning to Determine the Motorist Somnolence
DOI:
https://doi.org/10.54060/jieee.v4i1.88Keywords:
Fatigue Detection, OpenCV, Android Security, AndroidAbstract
Traffic accidents pose an increasing threat to society, and researchers are dedicated to preventing accidents and reducing fatalities, as highlighted by the World Health Organ-ization. One significant cause of accidents is drowsy driving, which often leads to severe injuries and loss of life. The objective of this research is to create a fatigue detection sys-tem that can effectively minimize accidents associated with exhaustion. The system uti-lizes facial recognition technology to identify drowsy drivers by analyzing eye patterns through video processing. When the level of fatigue surpasses a predetermined thresh-old, the system alerts the driver and adjusts the vehicle's acceleration accordingly. The implementation of OpenCv libraries, such as Haar-cascade, along with Raspberry Pi fa-cilitates seamless integration of the system. This dissertation evaluates advancements in computational engineering for the development of a fatigue detection system to miti-gate accidents caused by drowsiness. It offers valuable insights and recommendations to enhance comprehension and optimize the system's effectiveness, ultimately leading to safer road travel.
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Copyright (c) 2023 Sakshi Pandey, Dr. Sheenu Rizvi
This work is licensed under a Creative Commons Attribution 4.0 International License.
