Hand Gesture Recognition & Control

Overview

Hand Gesture Recognition & Control is an advanced AI-powered system that enables real-time recognition and interpretation of hand gestures for various applications such as gesture-controlled drones, AI-based human-computer interaction, and assistive technologies. This project integrates multiple deep learning techniques, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTMs), MediaPipe, YOLO, and ONNX, to achieve robust gesture recognition and control.

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Table of Contents

• Overview
• Features
• Installation
• Dataset
• Model Architecture
• Training Process
• Inference & Real-Time Gesture Recognition
• Gesture-Controlled Drone Simulation
• Real-Time Hand Pose Tracking
• Edge Deployment
• Future Improvements
• Conclusion

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Features

🔹 Real-Time Hand Gesture Recognition

• Utilizes CNN-based models and MediaPipe for robust hand tracking.
• Supports multiple hand gestures with precise landmark detection.

🔹 Gesture-Controlled Drone Simulation

• Enables drone control using recognized hand gestures in a simulated environment.
• Implemented using V-REP.

🔹 Dataset Collection and Training Pipeline

• Includes a diverse dataset of hand gestures for training.
• Covers various angles, lighting conditions, and backgrounds.

🔹 Edge Device Compatibility

• Optimized for deployment on embedded devices like Raspberry Pi and Jetson Nano.
• Supports ONNX model conversion for efficient inference.

🔹 Multi-Modal AI Fusion

• Future integration plans with voice and facial recognition.

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Installation

Prerequisites

Ensure that you have the following dependencies installed:

pip install -r requirements.txt

You may also need additional dependencies depending on the platform.

Clone the Repository

git clone https://github.com/your-repo/Hand-Gesture-Recognition-Control.git
cd Hand-Gesture-Recognition-Control

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Dataset

The dataset used for training consists of multiple hand gesture images labeled for different actions.

Hand Gesture Dataset Samples

Gesture Classes

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Model Architecture

The system architecture consists of:

• Convolutional Neural Networks (CNNs): Extracts spatial features from hand images.
• LSTMs: Processes sequences of hand movements for gesture recognition.
• MediaPipe: Provides real-time hand tracking and landmark detection.
• ONNX Models: Optimized models for deployment on edge devices.

Hand Landmarks Detection

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Training Process

The training pipeline includes dataset preprocessing, augmentation, model training, and evaluation.

python keypoint_classification.ipynb
python point_history_classification.ipynb

Training Visualization

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Inference and Real-Time Gesture Recognition

To run the inference:

python app.py

Real-Time Gesture Detection

Hand Gesture Recognition in Action

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Gesture-Controlled Drone Simulation

This project includes a simulated drone that can be controlled using hand gestures. The simulation is implemented in V-REP.

Simulation Video

Drone Simulation Framework

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Real-Time Hand Pose Tracking

A continuous hand tracking system allows gesture-based AI interaction.

Pose Tracking GIF

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Edge Deployment

This system supports deployment on edge devices such as Raspberry Pi or other embedded systems for real-time low-power inference.

Edge Deployment Image

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Future Improvements

• Multi-Modal Fusion: Integrate voice and gesture recognition for better interaction.
• Optimized Deployment: Convert models using TensorRT for improved efficiency.
• Customizable Gestures: Enable users to define and train their own gesture sets.

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Conclusion

Hand Gesture Recognition & Control is a robust foundation for real-time gesture recognition and AI-controlled applications. With future enhancements, it can be applied in robotics, AR/VR, smart environments, and assistive technologies. Contributions and feedback are welcome!

For any questions or contributions, feel free to open an issue or a pull request!