System for monitoring of parking spaces using computer vision

DOI: 10.31673/2412-4338.2024.045775

Abstract

This article explores the potential of a prototype system that monitors the occupancy of parking spaces. Through in-depth research, the paper highlights the problems faced by drivers and the shortcomings of traditional methods of solving them. To effectively achieve this goal, we rely heavily on numerous computer vision technologies. Image processing algorithms are used by the system to actively detect free or occupied parking spaces, and this happens in real time. Efficient processing of the video stream is provided by OpenCV libraries, which are used for image transformation, adaptive thresholding, contour analysis, and parking lot status identification. The system shows free parking spaces and displays information on the screen. Thanks to the use of this technology, the process of monitoring parking spaces is automated, resulting in a reduction in the time required to find free spaces, which optimizes the overall use of parking areas. The system can be very useful for improving modern smart transportation systems and can also help organize smart parking spaces. The system has great potential and prospects for further research, as well as integration into modern intelligent transportation systems, in particular for organizing smart parking lots and improving parking space management.

Keywords: system, computer vision, parking lot monitoring, OpenCV, image processing, video analytics, free parking space detection, intelligent transportation systems.

References

1. Giampaoli L. E., Hessel F. (2021) Parking Space Occupancy Monitoring System Using Computer Vision and IoT, IEEE 7th World Forum on Internet of Things (WF-IoT), New Orleans, LA, USA. pp. 7-12, doi: 10.1109/WF-IoT51360.2021.9595935.
2. Kuzela M., Fryza T., Zeleny O. (2024) Using Computer Vision and Machine Learning for Efficient Parking Management: A Case Study, 13th Mediterranean Conference on Embedded Computing (MECO), Budva, Montenegro. pp. 1-4, doi: 10.1109/MECO62516.2024.10577808.
3. Dixit M., Srimathi C., Doss R., Loke S., Saleemdurai M. A. (2020) Smart Parking with Computer Vision and IoT Technology, 43rd International Conference on Telecommunications and Signal Processing (TSP), Milan, Italy. pp. 170-174, doi: 10.1109/TSP49548.2020.9163467.
4. Popereshnyak S., Yurchuk I. (2021) Car Parking Data Processing Technique for Smart Parking System as Part of Smart City. Lecture Notes in Computational Intelligence and Decision Making. ISDMCI. Advances in Intelligent Systems and Computing, vol 1246. Springer, Cham.
5. Zhang, Y.; Chen, P. Path Planning of a Mobile Robot for a Dynamic Indoor Environment Based on an SAC-LSTM Algorithm. Sensors 2023. № 23, P. 9802.
6. Ganenko L. D., Zhebka V. V. Analytical review of issues of navigation of mobile robots in closed spaces. Telecommunications and information technologies. 2023. No. 3(80). Art. 85-98.
7. Malinov V., Zhebka V., Zolotukhina O., Franchuk T., Chubaievskyi V. Biomining as an Effective Mechanism for Utilizing the Bioenergy Potential of Processing Enterprises in the Agricultural Sector. CEUR Workshop Proceedings. 2023, 3421, p. 223–230