Vision-Based Automated Traffic Signaling

  • H. Mallika
  • Y. S. Vishruth
  • T. Venkat Sai KrishnaEmail author
  • Sujay Biradar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1154)


Traffic management has become one of the most complicated issues of recent times in metropolitan cities. Conventional traffic signaling systems are pre-programmed and alternate between red and green lights without any estimation of traffic. This signaling methodology leads to problems during peak hours at the intersections, where traffic ratio in a few lanes are dense when compared to others. Therefore, an efficient model is needed, which can manage the traffic flow at a certain point. The proposed model offers a solution using the CCTV footage from signal cameras to decongest traffic, based on a live estimate of traffic density. A state-of-the-art Deep Neural Network algorithm determines the number of vehicles and their type at a particular signal for object detection called You Only Look Once (YOLO), as it provided speed and accuracy in real-time. Based on vehicle count and road associated parameters, traffic density is computed to provide a dynamic extension of signaling time for a particular lane. Therefore, time saved from empty lanes is used to clear traffic on other busy lanes.


Density-based Signal cameras Adaptive Real-time Base Time Extension Time Traffic monitoring GUI 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • H. Mallika
    • 1
  • Y. S. Vishruth
    • 1
  • T. Venkat Sai Krishna
    • 1
    Email author
  • Sujay Biradar
    • 1
  1. 1.Electronics and Communication DepartmentMSRITBangaloreIndia

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