Hand Gesture Recognition for Drone Control 🚀 This project implements hand gesture recognition using computer vision and deep learning to control a drone. It utilizes MediaPipe and OpenCV for real-time hand tracking, extracts hand landmarks, and maps them to predefined gestures. The collected gesture data can be used to train a machine learning model for robust classification and future drone integration.

📌 Features ✔️ Real-time Hand Tracking using MediaPipe ✔️ Landmark Extraction for precise gesture recognition ✔️ Gesture Data Collection stored in CSV format ✔️ Predefined Gestures: up, down, flip, stop, throttle ✔️ Scalability: Extendable for more gestures and ML-based classification

🛠 Technologies Used Python 🐍 OpenCV – Computer vision for real-time video processing MediaPipe – Hand tracking and landmark extraction NumPy & Pandas – Data processing and storage Matplotlib (optional) – For data visualization 📂 Project Structure bash Copy Edit 📦 Hand Gesture Recognition Drone
│── 📂 gesture_data/ # Collected gesture dataset
│── 📜 AIMS-less-feature-drone.ipynb # Jupyter Notebook (Code Implementation)
│── 📜 README.md # Project Documentation
│── 📜 requirements.txt # Dependencies

🖐️ Gesture Collection Process The camera captures real-time video. MediaPipe detects hand landmarks. Gestures are labeled (up, down, etc.). The extracted data is stored in CSV format. This data can be used for training an ML model. 📈 Future Enhancements 🚀 Machine Learning Model: Train a classifier for gesture prediction 🚀 Integration with Drones: Implement communication with drone APIs 🚀 Voice Commands: Combine gesture & voice for hybrid control 🚀 Web Interface: Remote control via Flask or React

🔗 References MediaPipe Documentation OpenCV Official Website 📌 Developed by Aman kumar