International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Volume 7 Issue 2
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Oculus: AI-Powered Dynamic Traffic Signal Management System

Author(s) Ms. Anushka Patel, Ms. Anusha Nagar, Ms. Anoushka Vyas, Mr. Aaditya Panwar, Mr. Anupam Kumar Raushan
Country India
Abstract This study presents a novel solution—an AI
powered traffic management and signal monitoring system—to
the growing problems caused by the growing number of vehicles
and the resulting increase in traffic congestion worldwide. The
frequency of heavy traffic congestion at major junctions cause a
significant loss of man-hours in addition to interfering with traffic
flow. Our research focuses on putting in place a smart traffic
control system that uses real-time video processing techniques to
evaluate traffic density because we recognize the urgent need for
an effective traffic management system. Presenting a notable
improvement over the current manual traffic control methods is
the main goal. Our system's goal is to optimize signal timings in
real time by using artificial intelligence algorithms to dynamically
assess traffic circumstances. This will reduce congestion and save
valuable manhours lost in traffic jams. This study marks a
significant advancement in creating a more responsive and flexible
traffic control system that can enhance transportation networks'
effectiveness.
Keywords Gaussian mixture model, Shortest Job First, Initialize Foreground Detector, Detect Cars in an Initial Video Frame, Threshold, Traffic Density.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 2, March-April 2025
Published On 2025-04-14
DOI https://doi.org/10.36948/ijfmr.2025.v07i02.40395
Short DOI https://doi.org/g9fm43
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