This paper describes an image processing method that can
facilitate skill learning in karate using recorded karate competition videos.
The proposed method superimposes a partially filmed karate competition court in
the input video image onto an overall model of a karate court via a homography
transform. This method utilizes the Stacked Hourglass Network, a deep neural
network proposed for estimating human poses, to estimate the corresponding
points needed for the homography transform. To evaluate our method, a
player-focused video was augmented with complete competition field information.
The augmented video would be useful for observing both players’ actions as well
as the player positioning within the entire competition court. The evaluation
of the proposed method by a university karate club showed that it was useful
for skill learning.
Author(s) Details
Kazumoto Tanaka
Kindai University, Japan.
Please see the book here:- https://doi.org/10.9734/bpi/srnta/v6/2773
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