Abstract:
The intention with this project is to create an AI-powered system for car parking-detection that is able to detect available and occupied parking lots using computer vision and using predictive analytics. The system uses image processing algorithms and time-series forecasting models to deliver monitoring and future occupancy prediction. This report explores such techniques as vehicle detection, slot mapping, heat map generation and latter trend forecasting.
In the system architecture, it includes the description of capturing frames, detecting the occupancy of parking slot using trained models and visualization of trend in form of heat maps and charts. The primary programming language was Python, Flask provided the web interface. Prediction is made using ARIMA models to forecast future parking demands from the historical data. Additional features are image encoding; visual feedback of predicted usage patterns, and interactive data visualizations. The proposed solution helps to mitigate urban congestion and parking management in smart cities.
The implementation shows how the integration of AI can contribute to smarter infrastructure planning and user decision-making. the system seeks to improve the user experience by informing the user of available parking slot on a dashboard. System scalability enables its applicability to various types of parking environments, starting from small private lots up to large public facilities. This system can also be used in real-time.
