Urban parking inefficiencies have been exacerbated by rapid growth in vehicles based on human supervision. Smart parking system proposed utilizes computer vision and deep learning to identify occupied parking slots without per-slot hardware. Live video feeds from IP cameras are analyzed in a Flask-based micro service using OpenCV and a pre-trained Convolutional Neural Network. Every slot is virtually split and examined, with occupancy status determined by a confidence threshold improving classification accuracy and reducing false positives. The system has a three-layer architecture, users can make bookings using a web interface and receive a receipt with a specific QR code for entry and exit authentication. Experimental comparisons shows high reliability and lower user wait times, making it ideal for high-density urban areas. Administrators get live monitoring and slot analytics, allowing for optimal space utilization. SmartPark presents a pragmatic, future-proof answer that is congruent with smart city objectives and sustainable urban transport.
Smart Parking System, Real-Time Slot Detection, Computer Vision, Convolutional Neural Network (CNN), QR Code Verification.