Real-Time Gesture Recognition for Volume Control Using Python and ML Techniques

This research study proposes a gesture-based volume control system that combines computer vision and machine learning to improve user interaction with audio devices. By applying the OpenCV library for real-time video processing and MediaPipe's hand-tracking module, the system interprets hand gestures to control the volume. This system measures the distance between the index finger and thumb. Using this distance, the system adjusts the system's audio volume by using the Pycaw library. The purpose is to have a touchless interface wherein the user can control audio levels by simple hand movements to enhance accessibility and usability across various environments. This is an promising alternative to traditional input methods as the demand for more natural human-computer interaction grows, this gesture-based approach represents a leveraging computer vision and machine learning technique, which accurately detects gestures in volume control. The goal of this project is to design a touchless volume control system that relies on gesture input, computer vision, and machine learning techniques in order to bring an intuitive user experience. The proposed system integrates OpenCV for real-time video processing and makes use of Media Pipe's advanced hand-tracking abilities to identify gestures, which are the distance between the thumb and index finger, to control the volume of audio. Pycaw is used to interact with the audio output of the system to dynamically control the volume. This gesture-based interface is designed to improve accessibility, usability, and user experience by providing a modern, intuitive alternative to traditional volume control methods.