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- W2922366543 abstract "In this paper, we present a method that predicts the ball trajectory of a volleyball toss 0.3 s before the actual toss by observing the motion of the setter player. We input 3D data of body joints obtained using Kinect into a simple neural network, and 2D data estimated using OpenPose is used for comparison. We created simple neural networks for the two players and tested them. The trajectory of a volleyball toss is properly predicted by the proposed method and the error of the toss trajectory was approximately equal to the size of the ball. This technology can provide a new spectating experience in sports by superimposing the predicted images onto a live broadcast. We also show that this method can be used to identify the important body parts that contribute to the toss prediction. A professional volleyball analyst stated that this technology can be used for analyzing the peculiarities of opponent players." @default.
- W2922366543 created "2019-03-22" @default.
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- W2922366543 date "2019-03-11" @default.
- W2922366543 modified "2023-10-06" @default.
- W2922366543 title "Prediction of Volleyball Trajectory Using Skeletal Motions of Setter Player" @default.
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- W2922366543 doi "https://doi.org/10.1145/3311823.3311844" @default.
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