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arXiv · 2201.00095

Computer Vision Based Parking Optimization System

Siddharth Chandrasekaran·Jeffrey Matthew Reginald·Wei Wang·Ting Zhu
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Abstract

An improvement in technology is linearly related to time and time-relevant problems. It has been seen that as time progresses, the number of problems humans face also increases. However, technology to resolve these problems tends to improve as well. One of the earliest existing problems which started with the invention of vehicles was parking. The ease of resolving this problem using technology has evolved over the years but the problem of parking still remains unsolved. The main reason behind this is that parking does not only involve one problem but it consists of a set of problems within itself. One of these problems is the occupancy detection of the parking slots in a distributed parking ecosystem. In a distributed system, users would find preferable parking spaces as opposed to random parking spaces. In this paper, we propose a web-based application as a solution for parking space detection in different parking spaces. The solution is based on Computer Vision (CV) and is built using the Django framework written in Python 3.0. The solution works to resolve the occupancy detection problem along with providing the user the option to determine the block based on availability and his preference. The evaluation results for our proposed system are promising and efficient. The proposed system can also be integrated with different systems and be used for solving other relevant parking problems.

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Siddharth Chandrasekaran, Jeffrey Matthew Reginald, Wei Wang, Ting Zhu. 2022-01-01. Computer Vision Based Parking Optimization System. https://arxiv.org/abs/2201.00095

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