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Title |
The Literature Survey on Virtual Piano |
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Author |
Yashwanth G, Saifulla Khan, T V Rahul Reddy, Sukanya H A Varsha N |
| Citation |
Vol. 25 No. 4 pp. 75-80 |
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Abstract |
This paper presents an efficient data-driven approach to trace fingertip and detect finger tapping for virtual piano using an RGB-D camera. We collect 7200 depth images covering the foremost common finger articulation for enjoying piano, and train a random regression forest using depth context features of randomly sampled pixels in training images. within the online tracking stage, we firstly segment the hand from the plane in touch by fusing the knowledge from both colour and depth images. Then we use the trained random forest to estimate the 3D position of fingertips and wrist in each frame, and predict finger tapping supported the estimated fingertip motion. Finally, we build a kinematic chain and recover the articulation parameters for every finger. In contrast to the prevailing hand tracking algorithms that always require hands are within the air and cannot interact with physical objects, our method is meant for hand interaction with planar objects, which is desired for the virtual piano application. Using our prototype system, users can put their hands on a desk, move them sideways then tap fingers on the desk, like playing a true piano. Preliminary results show that our method can recognize most of the beginners piano-playing gestures in real-time for soothing rhythms. |
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Keywords |
Fingerend Tracking, Finger Beating Detection, Virtual Piano, RGB-D picture, Computer- Human Interaction. |
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