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Gesture Mouse Control Using Computer Vision | Python AI Project

Gesture Mouse Control



Technology is moving towards more natural and touchless ways of interaction. Gesture-based systems are a major step in this direction. The Gesture Mouse Control project allows users to control their computer mouse using simple hand gestures captured through a webcam. This project uses Python, OpenCV, and MediaPipe to create an intelligent and contact-free mouse control system.

Introduction

Traditional input devices such as a mouse and keyboard require physical contact. In many situations, such as medical environments or assistive technologies, touchless interaction becomes very important. Gesture recognition solves this problem by enabling systems to understand human hand movements and convert them into actions.

The gesture mouse system tracks hand movements in real time and maps them to mouse actions like cursor movement, clicking, and scrolling.

Problem Statement

Many users face difficulty using physical input devices due to disabilities or injuries. Additionally, shared devices raise hygiene concerns. The goal of this project is to create a low-cost, easy-to-use, and touchless mouse control system using a standard webcam.

Technologies Used

  • Python: Core programming language for development
  • OpenCV: Used for webcam access and image processing
  • MediaPipe: Provides accurate hand landmark detection
  • PyAutoGUI / Pynput: Used to control mouse actions

How the System Works

The webcam continuously captures video frames. Each frame is processed using OpenCV, and MediaPipe detects hand landmarks. Based on finger positions and distances, specific gestures are identified. These gestures are then converted into mouse movements and click events.

Hand Gesture Recognition

MediaPipe detects 21 hand landmarks including fingertips and joints. By analyzing the position of these landmarks, the system determines which fingers are raised or folded.

  • Index finger movement controls cursor position
  • Pinch gesture performs mouse click
  • Two-finger gesture enables right click
  • Vertical hand movement controls scrolling

Mathematical Logic

The system uses distance calculation between fingertips to detect gestures. The Euclidean distance formula is applied to determine how close two fingers are:

Distance = √((x₂ − x₁)² + (y₂ − y₁)²)

Applications

  • Assistive technology for disabled users
  • Touchless systems in healthcare
  • Smart classrooms and presentations
  • Human–computer interaction research
  • Virtual and augmented reality interfaces

Advantages

  • No physical contact required
  • Low-cost implementation
  • Works with standard webcams
  • Real-time performance

Limitations

  • Depends on good lighting conditions
  • May experience gesture fatigue
  • Accuracy reduces with cluttered backgrounds

Future Improvements

The project can be enhanced by adding support for custom gestures, multi-hand detection, and deep learning-based gesture classification. Integration with VR and AR systems is also a promising future direction.

Conclusion

The Gesture Mouse Control project demonstrates the power of computer vision and AI in creating natural user interfaces. It replaces traditional input devices with intuitive hand gestures, making interaction more accessible and hygienic. This project is ideal for students and developers exploring artificial intelligence and computer vision.

GitHub Repository:
https://github.com/Coding-with-Akrash/gesture-mouse

Author: Akrash Noor

LinkedIn: www.linkedin.com/in/akrash-noor

Email: akrashnoor2580@gmail.com

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Location: Wah Cantt., Wah, Pakistan

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