Title | Vehicular Propagation Velocity Forecasting Using Open CV |
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Authors | Udayan Das, Vandana Sharma, Madhabananda Das, Sushruta Mishra, Celestine Iwendi and Jude Osamor |
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Type | Conference paper |
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Abstract | 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. |
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Year | 2023 |
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Conference | 2023 4th International Conference on Computation, Automation and Knowledge Management (ICCAKM) |
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Publisher | IEEE |
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Accepted author manuscript | File Access Level Open (open metadata and files) |
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Publication dates |
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Published | 12 Dec 2023 |
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Published in print | 05 Mar 2024 |
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Journal | 2023 4th International Conference on Computation, Automation and Knowledge Management (ICCAKM) |
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ISBN | 9798350393248 |
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Digital Object Identifier (DOI) | https://doi.org/10.1109/iccakm58659.2023.10449587 |
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Web address (URL) | http://dx.doi.org/10.1109/iccakm58659.2023.10449587 |
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