An OpenCV-Driven Smart Parking Framework
DOI:
https://doi.org/10.32628/IJSRST251259Keywords:
CNN, YOLO, WSN, RFIDAbstract
The rapid rise in urban vehicle ownership has created major parking challenges, including congestion, time loss, and pollution. Traditional systems often rely on costly, non-scalable sensors and lack real-time monitoring. This paper presents a Smart Parking System (SPS) that uses computer vision, machine learning, and IoT to provide a scalable, accurate, and cost-effective solution. Leveraging cameras and a Convolutional Neural Network (CNN), the system detects parking occupancy with over 95% accuracy and guides drivers to the nearest available spot using an optimized distance algorithm. The architecture includes image capture, real-time processing via OpenCV, cloud storage, a mobile app, and an admin dashboard. Results show improved efficiency, reduced search time, and lower emissions, making SPS a strong candidate for smart city applications. Future enhancements include demand prediction and support for multi-level parking structures.
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References
M. Z. Abidin and R. Pulungan, "A Systematic Review of Machine-vision-based Smart Parking System," Scientific Journal of Informatics, vol. 7, no. 2, pp. 240-765, Nov. 2020.
S. Y. Siddiqui and M. A. Khan, "Smart Occupancy Detection for Road Traffic Parking Using Deep Extreme Learning Machine," Volume 34, issue, pp. 1-6.
R. K. Gupta and G. Rani, "Machine Learning and IoT Based Real-Time Parking System: Challenges and Implementation," 3rd International Conference on Innovative Computing and Communication (Icicc-2020), pp. 1-6. DOI: https://doi.org/10.2139/ssrn.3563377
Atta, S. Abbas, M. A. Khan, G. Ahmed, and U. Farooq, "An Adaptive Approach: Smart Traffic Congestion Control System," 2018.
Al-Dweik, R. Muresan, M. Mayhew, and M. Liebrman, "IoT-based Multifunctional Scalable Real-Time Enhanced Roadside Unit for Intelligent Transportation System," CCECE, pp. 1-6, 2017. DOI: https://doi.org/10.1109/CCECE.2017.7946618
Ata, M. A. Khan, S. Abbas, G. Ahmad, and A. Fatima, "Modelling Smart Road Traffic Congestion Control System Using Machine Learning Techniques," 2019. DOI: https://doi.org/10.14311/NNW.2019.29.008
L. Baroffio, L. Bondi, M. Cesana, A. E. Redondi, and M. Tagliasacchi, "A Visual Sensor Network for Parking Lot Occupancy Detection in Smart Cities," 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT), IEEE, pp. 745-750. DOI: https://doi.org/10.1109/WF-IoT.2015.7389147
Benevolo, R. P. Dameri, and B. D’Auria, "Smart Mobility in Smart City," in Empowering Organizations, Springer, Cham, pp. 13-28, 2016. DOI: https://doi.org/10.1007/978-3-319-23784-8_2
Y. Zheng, S. Rajasegarar, and C. Leckie, "Parking Availability Prediction for Sensor-enabled Car Parks in Smart Cities," 2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), IEEE, pp. 1-6. DOI: https://doi.org/10.1109/ISSNIP.2015.7106902
Kashif, M. A. Khan, S. Abbas, H. Zahid, and A. Fatima, "Intelligent Transportation System (ITS) for Smart Cities Using Mamdani Fuzzy Inference System," International Journal of Advanced Computer Science and Applications (IJACSA), vol. 9, no. 2, pp. 94-105, 2018, DOI: 10.14569/IJACSA.2018.090215. DOI: https://doi.org/10.14569/IJACSA.2018.090215
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