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This paper is about designing a real-time Intelligent Transport System (ITS) that detects wrong-way drivers on dangerous roads. Whenever such a car reaches the zone under surveillance, the system immediately detects it using just one CCTV camera. The detection device sends a warning signal to the drivers and alerts the monitoring centre simultaneously. The core of the proposed method consists of three key processes: detection, tracking, and validation, which are achieved through video imaging techniques. Detecting vehicles in video frames is achieved with a Deep Learning method named You Only Look Once (YOLOv3), trained on a custom dataset. The system tracks the vehicles' movements over time by means of linear quadratic estimation, known as Kalman filtering. In the final step, the trajectory of vehicles is found by an "entry-exit" algorithm that has been shown to recognize drivers moving in the wrong direction with an accuracy of 91.98%.
Keywords:
linear quadratic estimation (LQE), YOLOv3, intelligent transportation systems (ITS), convolutional neural networks (CNNs)
Cite Article:
"Real Time Traffic Detection of The Wrong Side Vehicle", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 9, page no.a811-a824, September-2025, Available :http://www.ijrti.org/papers/IJRTI2509094.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator