A block machine has been proposed as effective systems to support attack practice in volleyball. A method for manipulating the system by tablet operation has been established, and the use of the system has been shown to improve practice effectiveness. However, due to the requirement of manual operation, it has been reported that the efficiency of practice decreases due to the error between the block position and the attack position. Therefore, in order to operate the block machine automatically, we propose a method to acquire the player position from monocular video in real time and predict the attack position.
© Copyright 2018 VRST 2018: 24th ACM Symposium on Virtual Reality Software and Technology (VRST `18), November 28-December 1, 2018, Tokyo, Japan.. Veröffentlicht von Waseda University. Alle Rechte vorbehalten.