SOLVING URBAN PARKING CHALLENGES WITH MACHINE LEARNING
Author(s):
Ankit Puri, Abhinav Singh, Vedant Dave, Keerthi Mohan
Keywords:
Urban Parking, Machine Learning, Computer Vision, Convolutional Neural Networks, User-Centric Experience, Security Measures, Data-Driven Decision Making, Scalability, Sustainability.
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.
Article Details
Unique Paper ID: 162116

Publication Volume & Issue: Volume 10, Issue 8

Page(s): 19 - 24
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