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Table tennis and computer vision: a monocular event classifier
conference contributionposted on 2016-02-04, 11:26 authored by Kevin M. Oldham, Paul Chung, Eran Edirisinghe, Ben Halkon
© 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
Published inAdvances in Intelligent Systems and Computing
Pages29 - 32
CitationOLDHAM, K.M. ...et al., 2016. Table tennis and computer vision: A monocular event classifier. IN: Chung, P. ...et al.(eds.) Proceedings of the 10th International Symposium on Computer Science in Sports (ISCSS), Part 1, pp. 29-32.
- AM (Accepted Manuscript)
Publisher statementThis work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/
NotesThis paper is in closed access.
Book seriesAdvances in Intelligent Systems and Computing;392