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@article{180524,
        author = {Sana  Tabassum and Raafa Fatima and Rima Amina Yousuf and Dr Abdul Khadeer},
        title = {Intelligence Traffic Management System Using Opencv},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {1361-1366},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180524},
        abstract = {This project develops an Intelligent Traffic 
Management System utilizing OpenCV and deep 
learning techniques to enhance road safety and 
efficiency. By analyzing real-time traffic footage, the 
system detects and prevents traffic violations, including 
speed violations ,vehicle incoming and outgoing and 
helmet detection. 
The system employs computer vision and deep learning 
algorithms to process and analyze traffic footage. 
OpenCV is utilized for image processing and object 
detection, while deep sort techniques are trained on 
robust datasets to detect and classify traffic violations. 
Techniques such as YOLOv8 are used to generate: 
bonding boxes, class labels ,vehicle count, classification 
,vehicle speed and vehicle direction.  
The successful implementation of this system has the 
potential to revolutionize road safety and traffic 
monitoring, 
enhancing 
enforcement 
efficiency, 
improving driver behavior, and providing valuable 
insights for urban planning and infrastructure 
development. By automating violation detection, it 
integrates seamlessly with existing traffic infrastructure 
for smart city solutions, transforming the urban 
mobility experience.},
        keywords = {Intelligent Traffic Management, Deep  Learning, YOLOv8, OpenCV, Real-time Violation  Detection, Helmet Detection, Vehicle Speed Estimation,  Traffic Surveillance, Object Tracking, Deep SORT,  Road Safety, Computer Vision, Urban Planning,  Vehicle Classification, Direction Analysis.},
        month = {June},
        }