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  Published Paper Details:

  Paper Title

DrowseGuard Drowsiness Detection System: A Review Of Existing Systems And A Schema For Future Solution

  Authors

  Prof. Pritam Ahire,  Pratham Bhor,  Ishika Bansal,  Prayukti Dubey

  Keywords

road safety, face analysis, Computer Vision, Machine Learning, and driver sleepiness detection.

  Abstract


Driver drowsiness detection systems improve road safety by tracking driver attention in real time and providing timely warnings to reduce the risk of accidents. This research compares many methods for utilizing computer vision and machine learning techniques to identify driver weariness. Techniques that make use of physiological signals, driving performance measures, face analysis, and hybrid approaches are assessed for their viability and efficacy. The main conclusions show that hybrid methods that incorporate facial monitoring together with other modalities can identify tiredness with over 94% accuracy. Real-time processing, individual variability, and striking a balance between system performance and user acceptability continue to be obstacles. To fully implement driver sleepiness detection in a variety of real-world driving circumstances, more research into tailored, adaptive systems is necessary.

  IJCRT's Publication Details

  Unique Identification Number - IJCRTAF02042

  Paper ID - 261096

  Page Number(s) - 211-215

  Pubished in - Volume 12 | Issue 5 | May 2024

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Prof. Pritam Ahire,  Pratham Bhor,  Ishika Bansal,  Prayukti Dubey,   "DrowseGuard Drowsiness Detection System: A Review Of Existing Systems And A Schema For Future Solution", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.12, Issue 5, pp.211-215, May 2024, Available at :http://www.ijcrt.org/papers/IJCRTAF02042.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


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ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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