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.

Control your music player with hand gestures. Wave, swipe, or show a thumbs up to change tracks, adjust volume, and play/pause music using real-time computer vision and gesture recognition.

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.

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.

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.

Control your music without touching your device using real-time hand-gesture recognition. This project uses OpenCV + MediaPipe to track hand movements and map them to intuitive actions like: 👉 Swipe Right → Next Track 👈 Swipe Left → Previous Track ✊ Fist → Play / Pause ✋ Open Palm → Stop 👍 Thumbs-Up → Volume Up 👎 Thumbs-Down → Volume Down

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

MediaPipe-based project for controlling slides, adjusting system settings (brightness, volume), and managing multimedia using hand gestures. This hands-free solution demonstrates innovative applications of computer vision and gesture recognition for seamless interaction with a computer.

No description available

A real-time hand gesture recognition system to control system volume using OpenCV, MediaPipe, and Python.

The purpose of this project is to discuss a volume control using hand gesture recognition system based on hand tracking.

This project demonstrates a gesture-controlled speaker system using Arduino Uno. The system uses an APDS9960 sensor for gesture recognition to control speaker functions such as play, pause, or volume using hand gestures.

Real-time Hand Gesture Recognition system using OpenCV and MediaPipe to control system functions like volume and media playback.

Gestify: An intuitive gesture recognition system that lets you control Spotify with hand gestures. Using computer vision and AI, Gestify recognizes various hand gestures to manage music playback, volume, and more, bringing touch-free control to your listening experience.

Develop a Python application that allows users to control their system's volume using hand gestures. This system will utilize the cv2 library for video processing, mediapipe for hand gesture recognition, and pyautogui for interacting with the system’s volume control.

Interactive hand tracking application that creates a heads-up display (HUD) for controlling Spotify and apps using computer vision, hand gestures, and voice recognition. Features real-time gesture controls, volume adjustment, and draggable virtual windows.

This project enables intuitive volume adjustment using hand gestures detected by a webcam. It replaces traditional volume control methods with a more interactive approach, allowing users to manage their device's volume effortlessly through real-time gesture recognition.

A comprehensive Python application that transforms your webcam into a powerful desktop control interface using computer vision and hand gesture recognition. Control media playback, volume, scrolling, and navigation with simple hand gestures - no mouse or keyboard required!

Hand gesture recognition system using MediaPipe and OpenCV for real-time tracking via webcam. Detects and counts gestures, controls brightness, and adjusts volume. Requires Python ≤3.10, OpenCV, MediaPipe, pyautogui, screen_brightness_control, and pycaw. Includes modules for hand tracking and main recognition script.

This project implements a hand gesture recognition system to control the volume of your computer's audio output using real-time video input.

AI Gesture-Based Virtual Desktop Controller is a computer vision system that allows users to control their computer using hand gestures. Using MediaPipe for hand tracking and a machine learning model for gesture recognition, it enables touchless actions like mouse movement, clicks, volume, brightness, media control, and desktop navigation.

A Python-based virtual mouse using OpenCV, Mediapipe, and PyAutoGUI for gesture recognition. Features include cursor control, left/right click, scrolling, volume and brightness adjustment, and more—all through hand gestures. 🚀🎮

This project uses mediapipe to detect hand do various works by that like finger counter, sign language recognition and a gesture volume control.

This project implements a gesture-based control system to change the volume on a computer using hand tracking and gesture recognition. By leveraging the power of MediaPipe's hand tracking module, the application captures real-time hand movements through a webcam and tracks the hand landmarks.

Gesture and Voice Controlled Virtual Mouse using computer vision and speech recognition. Enables hands-free interaction with cursor movement, clicking, scrolling, zooming, and system control (brightness and volume) using a modular and multi-threaded design

This project is a Python application that combines gesture recognition and real-time audio visualization. It uses a webcam to detect hand gestures and controls the system volume based on the distance between the thumb and index finger. Additionally, it visualizes audio data in real-time using various visual effects.

No description available

Controlling Volume Using Hand Gesture Recognition

Volume Control Using Dynamic Hand Gesture Recognition

Volume control using hand gesture recognition in windows