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http://ir.lib.seu.ac.lk/handle/123456789/5989
Title: | Internet of things based automatic system for the traffic violation |
Authors: | Nizzad, A. R. M. Sameer, U. M. Suhath, S. M. Mohamed Nafrees, Abdul Cader Rankothge, W. H. Kehelella, P. H. Mansoor, C. M. M. |
Keywords: | Image Processing Internet of Things Artificial Intelligence Traffic violation ITS |
Issue Date: | 17-Feb-2022 |
Publisher: | IEEE |
Citation: | 5th International Conference on Electrical, Electronics, Communication, Computer Technologies and Optimization Techniques (ICEECCOT) 10-11, December 2021 p. 371-376. |
Abstract: | An efficient and effective motor traffic management is crucial for any Intelligent Transportation System (ITS) to reduce traffic violations. Scientific evidence suggests that exceeding the speed limit is the most important factor that impacts the severity, fatality and other risks associated with motor vehicle collisions. However, due to the lack of proper technology used in the transport sector, there are possibilities for traffic law violators get unnoticed. Therefore, this study provides a solution based on Internet of Things with Image Processing technology to process the vehicle registration number to uniquely identify the vehicles that are violating traffic laws. The outcome confirms that the architecture is viable for the low-cost automation of traffic fine for the speed violation. In addition, it is recommended to use more robust mechanism to capture the real time speed of any fast-moving vehicle. In conclusion, the proposed architecture with slight modification can be deployed for many commercial test cases such as traffic management, parking, vehicle counting, data privacy, data security and more. |
URI: | http://ir.lib.seu.ac.lk/handle/123456789/5989 |
ISBN: | 978-1-6654-3272-6 |
ISSN: | https://doi.org/10.1109/ICEECCOT52851.2021.9708060 |
Appears in Collections: | Research Articles |
Files in This Item:
File | Description | Size | Format | |
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Internet_of_Things_Based_Automatic_System_for_the_Traffic_Violation.pdf Restricted Access | 448.2 kB | Adobe PDF | View/Open Request a copy |
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