An Intelligent Raspberry-Pi-Based Parking Slot Identification System

Authors

DOI:

https://doi.org/10.4108/eetinis.v10i4.4294

Keywords:

Parking Detection, Smart Parking, Android App, Raspberry Pi

Abstract

A growing population necessitates more transportation, which pressures car parking spots. Parking is a problem for public places in cities, such as theatres, malls, parks, and temples. Even though several techniques have been suggested in publications, manual parking systems are still used in most places. For large locations where it is challenging to find open spaces, traditional parking arrangements need to be more archaic and convoluted. This might lead to heavy traffic, minor mishaps, and widespread accidents. In the modern era of sophisticated parking management systems, an automatic parking spot-detecting system has been introduced in an innovative format. Experts in computer vision are drawn to this emerging field to contribute. The system could tell if the automobile was fully or partially parked. Neither during the process nor afterward, human oversight is required. As parking management enters the modern era, computer vision is becoming increasingly critical. The parking system will not only make it easier for drivers to identify parking spaces but also enhance parking administration and monitoring. Vehicles will be able to observe available parking spots due to technology that monitors parking spaces. India and other emerging nations, as well as industrialized ones, have recently shown interest in smart cities. This article's smart auto parking system was conceived and implemented utilizing a Raspberry Pi and cameras placed in various parking spaces. Using a website and an Android app, this project creates and deploys a real-time system that enables vehicles to efficiently find and reclaim open parking spaces.

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Published

06-11-2023

How to Cite

Agarwal, R., Sharma, G., Singh, N., Nair, H. S., Daga, Y., & Lakshmi, D. V. (2023). An Intelligent Raspberry-Pi-Based Parking Slot Identification System. EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, 10(4), e1. https://doi.org/10.4108/eetinis.v10i4.4294