Gesture Recognition for Media Interaction: A Streamlit Implementation with OpenCV and MediaPipe.

Abstract

Abstract: This project aims to create a media player application that responds to hand gestures, using Python and the OpenCV library. The system taps into computer vision methods, like those used in depth-sensing cameras such as Kinect or Intel RealSense, to track and understand hand movements. It processes the depth data to extract hand features and employs machine learning (like CNNs or decision trees) to classify these into gestures. This lets the application accurately interpret user gestures and apply them to media commands—play, pause, volume, and more. All this works through a user-friendly interface that even lets users customize gestures for specific commands. The combo of OpenCV and Python enables an efficient and adaptable media control system. This fusion of computer vision and machine learning offers a seamless, natural way to navigate media playback, making it an immersive experience without needing physical controllers.