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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/20220

Title: Table tennis and computer vision: a monocular event classifier
Authors: Oldham, Kevin M.
Chung, Paul Wai Hing
Edirisinghe, Eran A.
Halkon, Ben J.
Keywords: Event classification
Table tennis
Computer vision
Optical flow
Issue Date: 2016
Publisher: © Springer
Citation: OLDHAM, 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.
Series/Report no.: Advances in Intelligent Systems and Computing;392
Abstract: © 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%.
Description: This paper is in closed access.
Version: Accepted for publication
DOI: 10.1007/978-3-319-24560-7_4
URI: https://dspace.lboro.ac.uk/2134/20220
Publisher Link: http://dx.doi.org/10.1007/978-3-319-24560-7_4
ISBN: 9783319245584
ISSN: 2194-5357
Appears in Collections:Closed Access (Computer Science)

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