File(s) not publicly available
Reason: This item is currently closed access.
Table tennis and computer vision: a monocular event classifier
conference contributionposted on 04.02.2016 by Kevin M. Oldham, Paul Chung, Eran Edirisinghe, Ben Halkon
Any type of content contributed to an academic conference, such as papers, presentations, lectures or proceedings.
© Springer International Publishing Switzerland 2016. Detecting events in table tennis using monocular video sequences for match-play officiating is challenging. Here a low-cost monocular video installation generates image sequences and, using the Horn-Schunck Optical Flow algorithm, ball detection and location processing captures sudden changes in the ball’s motion. It is demonstrated that each abrupt change corresponds to a distinct event pattern described by its combined velocity, acceleration and bearing. Component motion threshold values are determined from the analysis of a range of table tennis event video sequences. The novel event classifier reviews change in motion data against these thresholds, for use in a rules based officiating decision support system. Experimental results using this method demonstrate an event classification success rate of 95.9%.
- Computer Science