Real-Time Vehicle Detection and Counting System

Project Overview

This project implements a real-time vehicle detection and counting system using Python and OpenCV. The system detects and counts vehicles in motion in a video feed using MOG2 for background subtraction and contour detection. Vehicles are tracked and counted as they cross a predefined line in the video frame.

Key Features

• Real-Time Vehicle Detection: Detects vehicles in motion from a video stream.
• Accurate Counting Logic: Tracks and counts vehicles that cross a defined line in the frame.
• Bounding Boxes & Live Count Display: Displays bounding boxes around detected vehicles and updates the vehicle count in real time.
• Noise Reduction: Optimized to handle real-world footage with minimal noise and errors.
• Multi-Scene Compatibility: Works across various video scenes with different vehicle types.

Technical Highlights

• MOG2 Background Subtraction: Uses MOG2 to separate foreground (vehicles) from the background.
• Contour Filtering: Filters contours based on aspect ratio and bounding box dimensions to accurately identify vehicles.
• Line-Crossing Logic: Real-time vehicle counting when vehicles cross a predefined line.
• Optimized for Real-World Footage: Includes noise reduction strategies and performance optimizations.

Installation

To get started with this project, clone the repository and install the required dependencies:

git clone https://github.com/your-username/vehicle-detection.git
cd vehicle-detection
pip install -r requirements.txt

Dependencies:

• Python 3.x
• OpenCV
• NumPy
• Matplotlib (for visualizations)

Usage

1. Prepare Video Input: Place the video file you want to analyze in the project directory (or use the provided sample video).

2. Run the Vehicle Detection Script:

python main.py

3. Output: The script will display a live feed with bounding boxes around detected vehicles and a count of vehicles passing the line.

Project Structure

vehicle-detection/
│
├── main.py            # Main script for vehicle detection and counting
├── utils.py           # Utility functions for contour filtering, background subtraction, etc.
├── config.py          # Configuration file for system parameters (e.g., line position, video source)
├── vehicles.mp4       # Sample traffic video for testing
├── requirements.txt   # List of required dependencies
└── README.md          # Project documentation

Potential Applications

• Smart City Traffic Monitoring: Monitor and analyze traffic flows in urban areas.
• Urban Infrastructure Analytics: Understand vehicle movements and congestion patterns in city infrastructure.
• Intelligent Transport Systems (ITS): Use real-time vehicle counting for better traffic management and planning.

Contributing

If you want to contribute to this project, feel free to fork the repository, make improvements, and submit a pull request. Contributions are always welcome!