Traffic Vehicle Monitoring System Using CNN

Helmet violations and triple riding are major causes of road accidents. Manual enforcement is inefficient, requiring automation for better compliance. This project utilizes YOLO v8, OpenCV, and Tkinter to detect motorcycles, riders, and helmets from video frames. It identifies helmet absence and triple riding, displaying results via a user-friendly GUI. The system enhances real-time traffic monitoring, reducing manual effort and improving road safety.

Authors

Publication Details

Journal
International Journal for Research in Applied Science and Engineering Technology
Published
2025-03-25
DOI
https://doi.org/10.22214/ijraset.2025.67656
Primary Topic
IoT and GPS-based Vehicle Safety Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

Traffic Vehicle Monitoring System Using CNN

Prof. Aniket Gunjal
International Journal for Research in Applied Science and Engineering Technology
IoT and GPS-based Vehicle Safety Systems
article

Traffic Vehicle Monitoring System Using CNN

Prof. Aniket Gunjal
article en

Abstract

Helmet violations and triple riding are major causes of road accidents. Manual enforcement is inefficient, requiring automation for better compliance. This project utilizes YOLO v8, OpenCV, and Tkinter to detect motorcycles, riders, and helmets from video frames. It identifies helmet absence and triple riding, displaying results via a user-friendly GUI. The system enhances real-time traffic monitoring, reducing manual effort and improving road safety.

International Journal for Research in Applied Science and Engineering TechnologyVol. 13(3)
Openalex Percentile: Top 95%
IoT and GPS-based Vehicle Safety Systems
DOIOpen Access PDFOpenAlex
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Traffic Vehicle Monitoring System Using CNN — Prof. Aniket Gunjal · International Journal for Research in Applied Science and Engineering Technology (2025) | TGRS Research Map | TGRS