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@article{197827,
        author = {Ritik Sharma and Yash Tripathi and Rinku Agrawal and Yash Pratap Singh and Shagun Sharma},
        title = {AI-Based Hand Gesture Controlled Media Player},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {0-0},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197827},
        abstract = {Artificial Intelligence and Computer Vision are really changing the way we interact with computers. We usually control media players with things like keyboards, mice, remote controls or touch screens. Sometimes these ways of controlling things are not very convenient especially when we need to control things without touching anything.
In the few years people have been doing a lot of research on recognizing gestures. This is a part of how we interact with computers. Gesture-based systems let us talk to machines using movements of our hands instead of using physical devices. This project is about making a media player that we can control with hand gestures that are captured by a webcam. The system uses Computer Vision to find the parts of our hands and figure out where our fingers are, in real time.
The system we made uses Python, OpenCV and MediaPipe to track our hands and recognize gestures accurately. We assigned hand gestures to different things we can do with media players, like play pause make the volume louder or softer go to the next song and go to the previous song. Our system is designed to be easy to use, efficient and touchless so it is easy for people to control media players. Artificial Intelligence and Computer Vision are what make this possible.},
        keywords = {Artificial Intelligence, Computer Vision, Hand Gesture Recognition, OpenCV, MediaPipe, Human-Computer Interaction, Media Control System},
        month = {April},
        }