Analyzing Parking Solutions with AI and Computer Vision: A Smart Parking Framework

DOI

10.2991/978-94-6463-858-5_133How to use a DOI?

Keywords

Smart Parking; AI; Computer Vision; Automation; License Plate Recognition; QR Code

Abstract

Smart parking systems are a new avenue to address urban parking challenges while maximizing the efficiency of space usage and reducing traffic congestion. This paper performs a systematic literary review (SLR) regarding on-going advancement of smart parking systems, which includes technology-enabled solutions: (e.g., sensor-enabled, vision-enabled, and IoT- enabled). It systematically reviews the various methodologies, architectures, and algorithms used in smart parking management. The adept recognition and detection of real-time parking availability, reservation systems, pricing approaches, and mobile app integration are essential, therefore we critique these important factors. Major challenges, including infrastructural costs, data safety concerns, and barriers to adoption are discussed.

In this work, we present a Smart Parking System that utilizes Artificial Intelligence (AI) and Computer Vision to revolutionize urban parking management. We utilize real-time video feeds and algorithms like YOLOv8 and Mask R-CNN in order to execute correct parking space identification and vehicle classification. We optimize efficiency via an AI-driven traffic awareness-based dynamic guidance system coupled with AI-driven demand forecasting using Long Short-Term Memory (LSTM) networks and Random Forests, resulting in notably lower search time for vacant parking.

Compared to traditional IoT sensor-based systems, our approach is more cost-effective and scalable. This architecture gives a basis for further development of urban mobility, like future integration with autonomous vehicles.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Vaishali Tomar
AU  - Ismita Shrestha
AU  - Ashish Jain
PY  - 2025
DA  - 2025/11/04
TI  - Analyzing Parking Solutions with AI and Computer Vision: A Smart Parking Framework
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 1643
EP  - 1654
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_133
DO  - 10.2991/978-94-6463-858-5_133
ID  - Tomar2025
ER  -