This project uses Raspberry Pi equipped with a camera monitors parking spaces, detecting vehicle movement using computer vision. Real-time occupancy data is fed into a booking website, allowing users to reserve available spots. Python and OpenCV handle image processing and car detection, while the backend manages user information and bookings.

Raspberry Pi Sensor Overview:
The Raspberry Pi 3B+ is equipped with a Pi Camera module to monitor and detect vehicle movement within parking areas. Detailed Process:

• Camera Setup: The Pi Camera module is connected to the Raspberry Pi 3B+. It captures video footage of the parking spaces.
• Image Processing: Using computer vision algorithms (e.g., OpenCV), the Raspberry Pi analyzes the captured video frames to detect the presence or movement of vehicles.
• Detection Logic: Algorithms are programmed to recognize changes in pixel intensity or motion patterns that indicate a vehicle entering or leaving a parking space.
• Output: When a vehicle is detected, the system updates the parking occupancy status in real-time, which can be further processed by other components of the system.

Booking Website Overview: A user-friendly website enables remote booking of parking slots, enhancing convenience for users. Detailed Process:

• User Interface: The website provides an intuitive interface where users can view available parking slots, select a desired slot, and make a booking.

• Booking Management: Upon selection, the booking system communicates with the backend server to reserve the chosen parking slot.

• Confirmation: Users receive confirmation of their booking via email or SMS, including details such as slot number and duration.

• Integration: The booking website interfaces with other system components to ensure real-time updates on slot availability and occupancy status.

• Backend Processes Overview: Python programming language uses OpenCV and YOLO for car detection and backend image processing to manage user information and parking slot bookings. Detailed Process: Car Detection and Image Processing: OpenCV is used along with the haarcascade_cars.xml file for initial car detection. Three YOLO files are employed for further processing: A Python file for car detection A configuration file A weights file