Vehicular propagation velocity forecasting using open CV

Udayan Das, Vandana Sharma, Madhabananda Das, Sushruta Mishra, Celestine Iwendi, Jude Osamor

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This work presents a predictive learning driven methodology for recognizing the vehicular velocity. The developed model uses machine vision models to trace and detect vehicular movement in timely manner. It further deploys a machine tested framework for estimation of its velocity on basis of the accumulated information. The technique depends upon a CNN model that is validated with a standardized instances of vehicular scans and corresponding velocity parameters. The proposed model generates good efficiency and robustness in determining velocities across test conditions which encompass various kinds of vehicles and lighting scenarios. An optimal vehicular frequency is noted with heavy-weight vehicles in place in comparison to other vehicles. A mean latency period of 1.25 seconds and an error rate of 0.05 is observed with less road traffic in place. The suggested approach can be of great help in transportation systems, traffic monitoring and enhancing road safety.
Original languageEnglish
Title of host publicationProceedings of ICCAKM 2023: 4th International Conference on Computation, Automation and Knowledge Management
Number of pages7
ISBN (Electronic)9798350393248
ISBN (Print)9798350393255
Publication statusPublished - 5 Mar 2024
Event2023 4th International Conference on Computation, Automation and Knowledge Management (ICCAKM) - Amity University, Dubai, United Arab Emirates
Duration: 12 Dec 202313 Dec 2023 (Link to conference website)


Conference2023 4th International Conference on Computation, Automation and Knowledge Management (ICCAKM)
Abbreviated titleICCAKM
Country/TerritoryUnited Arab Emirates
Internet address


  • vehicle speed detection
  • machine learning
  • computer vision
  • convolutional neural network

ASJC Scopus subject areas

  • Information Systems and Management
  • Artificial Intelligence
  • Control and Optimization
  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications
  • Computer Science Applications


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