https://matjournals.net/engineering/index.php/ARCEI/issue/feed 2026-05-19T12:23:50+00:00 Open Journal Systems <p><strong>ARCEI</strong> is a peer-reviewed journal in the field of Telecommunication Engineering published by MAT Journals Pvt. Ltd. ARCEI is a print e-journal focused towards the rapid publication of fundamental research papers in all areas of communication engineering. This journal involves the basic principles dealing with the development and operation of communications technology, including telecommunications and computer programming. The Journal aims to promote high-quality research, review articles, and case studies mainly focusing on design and fabrication of devices, installation, operation and maintenance of electronics, equipment and systems, Embedded systems, Electronic equipment’s, process industries- For instrumentation and control of electronic devices, manufacturing- PCB, IC. The Journal involves comprehensive coverage of all the aspects of communication engineering.</p> https://matjournals.net/engineering/index.php/ARCEI/article/view/3586 2026-05-19T12:07:20+00:00 Viswanatha V viswanatha.v@nmit.ac.in Ramachandra A. C. viswanatha.v@nmit.ac.in Relnagi Mahes Satya Venkat Gowd viswanatha.v@nmit.ac.in Samvartha G. Puthuraya viswanatha.v@nmit.ac.in

<p><em>Rapid urbanization and the continuous increase in vehicle population have created major challenges in traffic monitoring and road management. Conventional manual vehicle counting methods require significant human effort, consume time, and often lead to inaccurate results under heavy traffic conditions. To address these limitations, this study presents a real-time vehicle counting system using computer vision techniques for automated traffic analysis. The proposed system processes live or recorded video streams captured from roadside cameras and identifies moving vehicles using an object detection framework. Detected vehicles are tracked across consecutive frames, and a counting mechanism is applied when vehicles cross a predefined virtual line. The system is designed to operate efficiently under varying traffic densities and provides instant vehicle count information for monitoring purposes. Experimental evaluation demonstrates that the model achieves reliable counting accuracy with low processing delay, making it suitable for real-world deployment. The developed solution can support traffic signal optimization, congestion analysis, parking management, and smart city transportation planning. Future enhancements may include vehicle classification, speed estimation, and integration with cloud-based analytics platforms.</em></p>

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