AI Hand Controller uses Computer Vision to recognize hand gestures and control various functions on your computer. The application can control system volume and YouTube video playback speed through hand gestures via webcam.

Gesture recognition helps computers to understand human body language. This helps to build a more potent link between humans and machines, rather than just the basic text user interfaces or graphical user interfaces (GUIs). In this project for gesture recognition, the human body's motions are read by computer camera. The computer then makes use of this data as input to handle applications. The objective of this project is to develop an interface which will capture human hand gesture dynamically and will control the volume level. For this, Deep Learning techniques such as Yolo model, Inception Net model+LSTM, 3-D CNN+LSTM and Time Distributed CNN+LSTM have been studied to compare the results of hand detection. The results of Yolo model outperform the other three models. The models were trained using Kaggle and 20% of the videos available in 20 billion jester dataset. After the hand detection in captured frames, the next step is to control the system volume depending on direction of hand movement. The hand movement direction is determined by generating and locating the bounding box on the detected hand.

Here's a complete Python project for a Hand Gesture Volume Controller using webcam. This project uses OpenCV, MediaPipe, and pycaw (for controlling system volume on Windows). It adjusts the system volume based on the distance between the thumb and index finger.

Gesture Controller using hand gestures for system control with Python, MediaPipe, and OpenCV. Interact with your computer through intuitive gestures for mouse movement, clicking, volume adjustment, and brightness control. 🖐️🖱️

This project aims to control the system volume and screen brightness using hand gestures. Use your left hand for brightness control and right hand for volume control. For detection of hand landmarks, mediapipe library is used.

HorizonGest is a real-time hand gesture recognition system built with C++ and OpenCV. This project enables users to adjust system settings like volume and screen brightness through hand gestures detected via webcam, making system control effortless and touch-free.

This repository provides a robust solution for controlling your mouse and audio volume using hand gestures, leveraging the advanced features of Mediapipe and OpenCV. With real-time hand tracking and gesture recognition, users can effortlessly browse through the system and adjust volume levels by simply moving their hand.

Hand Gesture Volume Control is a Python project that allows you to control the system's audio volume using hand gestures detected from a webcam input.

Volume Control Gesture lets users adjust system volume using hand gestures 🖐️🎚️. It uses OpenCV, MediaPipe, and Pycaw for real-time tracking. Simply move your fingers to increase or decrease the volume! 🔊

Volume Control using OpenCV | Python This project implements a hand gesture-based volume control system using OpenCV and Python. It detects hand movements via a webcam feed and automatically adjusts the system volume. Perfect for those interested in computer vision and gesture recognition.

A real-time hand gesture and distance-sensor-based mouse, scroll, and volume controller using Python, OpenCV, MediaPipe, and Arduino. This hybrid system enables touchless control of PC functions via hand gestures and ultrasonic sensor inputs.

Turn your hand into a mouse and a paintbrush. 🖐️✨ A real-time gesture recognition system for drawing, volume control, and clicking, powered by MediaPipe and Keras.

This project presented a program that allowed user to perform hand gestures for easy Volume control. It is a vision-based hand Gesture system that does not require any special markers or gloves and can operate in real-time on a commodity PC/ laptop with low-cost cameras.​

Hand gesture based system volume control using OpenCV, MediaPipe, and Pycaw

Real-time hand gesture-based volume control system using computer vision for intuitive and touchless human–computer interaction.

🚀 Hand Gesture Volume Control: A Flask web app using OpenCV, pycaw, and hand tracking for real-time volume adjustment via gestures. Stream video, detect hand positions, and control system volume seamlessly. Explore Python libraries like Flask, OpenCV, numpy, and pycaw in this interactive project! 🌟

Hand Gesture-Driven Volume System lets users adjust their computer’s volume using hand gestures, providing real-time visual feedback on a web interface. It combines gesture tracking, audio control, and live updates to create an intuitive and interactive experience.

My latest project: a Hand Gesture Volume Controller using Python, OpenCV, and MediaPipe! 🤚🎶 This innovative project leverages real-time hand tracking and gesture recognition to adjust system volume seamlessly. By detecting the distance between the thumb and index finger, the volume can be controlled intuitively and contactlessly.

This Python program utilizes the power of OpenCV to control system volume based on hand gestures captured by your camera. By tracking specific gestures, it provides an intuitive and hands-free approach to adjusting volume levels. Feel free to explore the code, contribute, and enhance the functionality.

This project enables gesture-based control of media playback (play/pause) and volume adjustment (up/down) using hand gestures. By detecting hand movements, the system allows users to interact with their media devices without needing to touch any buttons, offering a more intuitive and hands-free experience. Features: Play/Pause Controll and volume.

This project implements a multimodal interface for controlling music playback, integrating real-time hand gesture recognition (computer vision) and voice command processing (speech recognition). The system enables intuitive, hands-free media interaction, including play/pause, volume control, and track switching.

Used OpenCV , Google's mediapipe to Track Hand Gesture to Control System Volume

Helps you control your system volume, by using hand gestures. Made using OpenCV.

Hand Gesture Volume Control – A real-time system that lets you adjust your device's volume using hand gestures, powered by OpenCV, MediaPipe, and Pycaw.

Hand gesture → system control using MediaPipe/OpenCV. Real-time CV pipeline — maps gestures to actions like volume, scroll, or mouse control.

Arcane gesture recognition system that allows you to control media playback and system volume using intuitive hand gestures detected through your webcam.

A computer vision-based system that allows you to control the volume of your computer using hand gestures.

A Python-based volume controller using OpenCV and MediaPipe that allows you to control your system volume with simple hand gestures via webcam.

"Hand Gesture Volume Control" allows adjusting system volume via hand gestures captured by a webcam. Visual feedback with a volume bar and notifications for maximum/minimum levels are provided. Developed collaboratively by Riya Layek, Parul Srivastava, and Madhurima Mukherjee.

Control your system volume using simple hand gestures! GestureVolume leverages OpenCV, MediaPipe, and system commands to adjust volume dynamically based on finger distance. No need to touch your keyboard or mouse