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@article{162116,
        author = {Ankit Puri and Abhinav Singh and Vedant Dave and Keerthi Mohan},
        title = {SOLVING URBAN PARKING CHALLENGES WITH MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {8},
        pages = {19-24},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=162116},
        abstract = {The "Solving Urban Parking Challenges with Machine Learning" project pioneers a transformative approach in parking management, integrating cutting-edge technologies to optimize space utilization and enhance urban mobility. Employing advanced machine learning algorithms and computer vision techniques, this system revolutionizes parking space allocation, user experience, and urban planning. Embracing real-time vehicle detection through Convolutional Neural Networks (CNNs), predictive parking management, and robust security measures, this "Automated Parking System" redefines traditional parking landscapes. The synopsis delineates key features, user interface design, security protocols, and underlying technologies, including frameworks like YOLO, OpenCV, Pandas, and polygon testing. This project's scope spans technological advancements, user-centric experiences, security measures, data-driven decision-making, scalability, sustainability, and system requirements, ensuring a holistic and innovative solution to urban parking challenges.},
        keywords = {Urban Parking, Machine Learning, Computer Vision, Convolutional Neural Networks, User-Centric Experience, Security Measures, Data-Driven Decision Making, Scalability, Sustainability.},
        month = {},
        }