Background Subtraction Application

This project demonstrates real-time background subtraction using the mean frame technique. It captures video from a webcam, computes the mean of the last 50 frames to estimate the background, and extracts the foreground by comparing the current frame with the computed background. A trackbar is provided to dynamically adjust the threshold for foreground detection.

Features

• Real-time background subtraction using a webcam feed.
• Dynamic threshold adjustment using a trackbar.
• Efficient handling of frames using a rolling buffer (last 50 frames).
• Visualization of the original frame, computed background, and extracted foreground.

Technologies Used

• Python: Core programming language.
• OpenCV: For video capture, image processing, and visualization.
• NumPy: For efficient array operations and mean computation.

Installation and Setup in VS Code

Follow these steps to set up and run the project in VS Code:

1. Clone the Repository

Open a terminal in VS Code and run the following command to clone the repository:

git clone https://github.com/agarwal-prakhar/Background-Subtraction-Application.git
cd Background-Subtraction-Application

2. Set Up a Virtual Environment

Create and activate a virtual environment to manage dependencies:

On macOS/Linux:

python3 -m venv venv
source venv/bin/activate

On Windows:

python -m venv venv
venv\Scripts\activate

3. Install Dependencies

Install the required Python packages using pip:

pip install -r requirements.txt

4. Run the Application

Open the script Gausian_Mixture_Model.py , Frame_Differencing.py ,and others in VS Code and run it:

• Click the Run button in the top-right corner of VS Code.
• Alternatively, use the terminal to run the script:

python Gausian_Mixture_Model.py
python Frame_Differencing.py

5. Use the Application

• A window will open showing the live video feed, computed background, and extracted foreground.
• Use the trackbar to adjust the threshold for foreground detection.
• Press the q key to exit the application.

Troubleshooting

Webcam Not Detected

• Ensure your webcam is connected and accessible. Test it using other applications like Zoom or your system's camera app.

Dependency Issues

• Make sure all dependencies are installed correctly. If you encounter issues, try reinstalling them:

pip install --upgrade opencv-python numpy

Inconsistent Indentation

• If you encounter indentation errors, ensure your editor is configured to use 4 spaces for indentation (not tabs).