Real-Time Parking Availability Assessment Using Computer Vision and Image Analysis
Keywords:
Parking Space Detection, Real-Time Monitoring, Computer Vision, Deep Learning, Convolutional Neural Network (CNN), Smart Parking System, Occupancy Detection, Image Processing, Region of Interest (ROI), Edge Computing, Video Analytics, Traffic Management, Intelligent Transportation Systems, Urban Mobility, Automated Surveillance.Abstract
This paper proposes an Intelligent Real-Time Parking SpaceOccupancy Detection (IRPSOD) algorithm based oncomputer vision techniques for smart parking managementsystems. The proposed method utilizes a deep convolutional neural network (CNN) to automatically detect and classifyparking spaces as occupied or vacant from live video feeds.
References
[1] F. Liao, Y. Sun, Y. Wu, and J. Wang, "Real-time Occupancy Detection Of On-street Parking Spaces Based On An Edge Device," in 2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML), Hangzhou, China, 2022, pp. 621–625. https://doi.org/10.1109/CACML55074.2022.00109
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Published
2025-11-20
How to Cite
G.Prasanthi,P.Revathi,Dr. P.Avinash ,R. Sreedhar. (2025). Real-Time Parking Availability Assessment Using Computer Vision and Image Analysis. Journal of Computational Analysis and Applications (JoCAAA), 34(11), 1003–1013. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5895
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