Computer Vision Techniques

This repository contains implementations of various computer vision techniques including Background Subtraction, Mean Shift, and Optical Flow. These methods are widely used in image processing and video analysis for tasks such as object tracking, motion detection, and segmentation.

Table of Contents

• Introduction
• Folder Structure
• Installation
• Contributing
• License

Introduction

This repository provides implementations of three key computer vision techniques:

• Background Subtraction: A method used to separate foreground objects from the background in video sequences.
• Mean Shift: A non-parametric feature-space analysis technique used for clustering and object tracking.
• Optical Flow: A method used to estimate the motion of objects between consecutive frames in a video sequence.

Structure

├── background_subtraction
│   ├── main.py
├── meanshift
│   ├── MeanShiftTracking.py
├──optical_flow
│    ├── optical_flow_model.py
└──README.md

Installation

To get started with the project, follow these steps:

1. Clone the repository:

   $ git clone https://github.com/toanuitt/CV.git

2. Navigate to the project directory:

   $ cd CV

3. Install the required packages:

   $ pip install -r requirements.txt

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.

License

This project is licensed under the MIT License.