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@article{169901,
        author = {Nitya Munshi and Aftab Naik and Om Mungelwar and Prathamesh Murkute and Asmita Muthal and Swati Joshi},
        title = {Automated Parking management system using machine vision},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {2668-2671},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169901},
        abstract = {As the parking industry continues to evolve for an increasing number of cities struggle with traffic congestion and inadequate parking availability. Up to 30% of traffic in congested urban areas is due to drivers looking around in search of parking spaces. Searching for a parking space becomes increasingly difficult. Leading to development of  smart technologies that can help drivers find a parking spot efficiently, not only reducing traffic congestion but also the subsequent air pollution. Existing solutions require multiple sensors in every parking spot to address the automatic parking spot detection problems. However, their costs are very high, especially for large parking structures. A wide variety of other technological innovations like license plate detection, digital parking meter, and vision-based parking spot detection are included in this paper. In this paper, we propose to design naive distributed cameras, edge computing, Data analysis techniques to accomplish this task. Specifically, we deployed cameras to capture to identify which slots are occupied; cameras with wide angle fish-eye lenses will monitor the large parking lot via openCV and YOLO. We further optimize the algorithm and enable the real-time deep learning inference. Through this system a significant cost of existing systems can be saved.},
        keywords = {smart cities, smart parking, Secure Payment, Microcontrollers, OpenCV, YOLO.},
        month = {November},
        }