🎵 Gesture-Controlled Media Player

A real-time gesture-controlled media player that allows users to control system media
(play/pause, volume, next/previous track) using hand gestures captured via webcam.

This project demonstrates computer vision, real-time processing, gesture recognition, and OS-level automation using Python.

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🚀 Key Features

• ✋ Open Palm → Play / Pause
• 👍 Thumb Up → Volume Up
• 👎 Thumb Down → Volume Down
• 👉 Swipe Right → Next Track
• 👈 Swipe Left → Previous Track
• 🧠 Gesture stability using multi-frame confirmation
• ⚡ Real-time performance with FPS counter
• 🖥️ OS-level media control (works with Spotify, YouTube, VLC, etc.)

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🧠 System Workflow

Webcam Feed -> OpenCV Frame Processing -> MediaPipe Tasks API (21 Hand Landmarks) -> Finger State Detection -> Static & Dynamic Gesture Classification -> Gesture Stabilization (N-frame window) -> Media Control via PyAutoGUI

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Project Architecture

gesture-controlled-media-player/ ├── models/ │ └── hand_landmarker.task # MediaPipe hand landmark model │ ├── src/ │ ├── camera.py # Webcam handling (OpenCV) │ ├── hand_tracker.py # Hand landmark detection (MediaPipe) │ ├── gesture_utils.py # Gesture logic, swipe detection, stability │ ├── media_controller.py # OS-level media automation │ └── main.py # Application entry point │ ├── requirements.txt # Project dependencies ├── README.md # Project documentation └── venv/ # Virtual environment (ignored in Git)

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⚙️ Installation & Setup

1️⃣ Clone Repository

git clone cd gesture-controlled-media-player

2️⃣ Create & Activate Virtual Environment

python -m venv venv venv\Scripts\activate # Windows

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣ Run Application

python src/main.py 🧪 Supported Gestures Gesture Action Open Palm Play / Pause Thumb Up Volume Up Thumb Down Volume Down Swipe Right Next Track Swipe Left Previous Track

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📈 Performance & Stability Real-time hand tracking using MediaPipe Tasks API

FPS counter for performance monitoring

Gesture stabilization using sliding window (N-frame confirmation)

Cooldown & debounce logic to prevent accidental triggers

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🧠 Technical Highlights (Resume Keywords) Computer Vision

Real-Time Video Processing

Hand Landmark Detection

Gesture Recognition

MediaPipe Tasks API

OpenCV

OS Automation

Performance Optimization

Modular Python Design

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📌 Future Enhancements Gesture-controlled virtual mouse

Smart home device integration

Custom gesture training using ML models

Cross-platform support (Linux/macOS)

👨‍💻 Author

Kumar Aditya

📌 Aspiring Software / Full Stack / Computer Vision Developer