A Study on Smart Parking Space Allocator and Parking Management Using Opencv

Authors

  • P. K. Sheela Shanthakumari, P. Selvarani, J. Senthil Murugan, W. T. Chembian, M. Mithun Kumar, M. Karthikeyan, M. Govindaraj

DOI:

https://doi.org/10.17762/msea.v72i1.1685

Abstract

In today's society, the number of vehicles on the road is rising quite quickly every single day. Due to the small number of parking spots compared to the rising number of vehicles, finding a suitable parking space can be difficult and time-consuming. Traffic congestion is the outcome of this. According to this area's research, drivers only go a half-mile at a speed of 10 mph while seeking for parking, spending an average of 15 minutes doing so. Successful implementation of smart parking solutions can greatly decrease these issues. The traditional method requires the installation of multiple sensors at each parking lot, which is not only expensive but also quite challenging.This study presents a smart parking system based on image processing for open parking lots, multi-story parking garages, and other applications. Edge detection and coordinate bound pixel sections are combined in the suggested system design to assess whether a parking space in the obtained footage is occupied or not. It also illustrates the process of text to image conversion. Tesseract is used to extract text from the processed image. The variable level of image processing makes sure that various photos receive varying degrees of processing in order to produce text results that are optimum.

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Published

2023-01-17

How to Cite

P. K. Sheela Shanthakumari, P. Selvarani, J. Senthil Murugan, W. T. Chembian, M. Mithun Kumar, M. Karthikeyan, M. Govindaraj. (2023). A Study on Smart Parking Space Allocator and Parking Management Using Opencv. Mathematical Statistician and Engineering Applications, 72(1), 98–104. https://doi.org/10.17762/msea.v72i1.1685

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Section

Articles