Integrated Car Parking System

Smart parking systems are crucial for efficient urban mobility, reducing congestion, and improving parking management. This project implements an AI-based smart parking detection and surveillance system using computer vision and deep learning techniques. The system aims to detect available parking spaces, monitor security, and assist drivers in finding parking spots through a user-friendly interface. The project follows several key steps. The system employs deep learning models to detect and classify parking spaces in real-time. OpenCV and machine learning techniques are used to analyze video feeds, identify parked vehicles, and determine available slots. The model is trained on the dataset, which is of parking lot images, letting it learn spatial patterns and improve recognition accuracy. During system deployment, real-time video processing is conducted to provide live updates on parking availability. The surveillance aspect ensures security by detecting unauthorized vehicle movements or violations. The evaluation of the trained model is done using test datasets to measure performance in terms of detection accuracy and response time. Additionally, the system integrates a mobile or web application, enabling users to check parking availability and book spots remotely. This project contributes to smart city initiatives by optimizing parking space utilization, reducing traffic congestion, and enhancing security. Future improvements will incorporate 3D object detection for better accuracy and automation. The proposed solution demonstrates a practical approach to modernizing parking management with AI-driven intelligence.