Field
AI-Enabled Solutions
Date
April 2026
Abstract
Parking availability is a common challenge on university campuses, leading to congestion and time loss. This project proposes a camera-based parking monitoring system that uses machine learning to estimate parking availability. A YOLO model is applied to detect vehicles from an oblique elevated video, and a tracking method combined with a two-line counting logic is used to identify entry and exit events. The system updates parking status in real time and displays availability levels on a web-based dashboard.
This project is developed as a prototype and feasibility study, focusing on system design and practical implementation. Privacy considerations under California regulations are addressed by limiting the system to vehicle-level detection and avoiding facial and license plate recognition.
Recommended Citation
Yang, Li Min and Ngo, Anh, "Machine Learning Parking Monitoring System for UOP Campus" (2026). Pacific Innovation and Entrepreneurship Summit (PIES). 60.
https://scholarlycommons.pacific.edu/pies/60
