Table tennis and computer vision: A monocular event classifier

Oldham, KM; Chung, PWH; Edirisinghe, EA; Halkon, BJ

Table tennis and computer vision: A monocular event classifier

Oldham, KM Chung, PWH Edirisinghe, EA Halkon, BJ

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Publication Type:: Conference Proceeding
Citation:: Advances in Intelligent Systems and Computing, 2016, 392 pp. 29 - 32
Issue Date:: 2016-01-01

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Field	Value	Language
dc.contributor.author	Oldham, KM	en_US
dc.contributor.author	Chung, PWH	en_US
dc.contributor.author	Edirisinghe, EA	en_US
dc.contributor.author	Halkon, BJ https://orcid.org/0000-0001-5459-2329	en_US
dc.date.issued	2016-01-01	en_US
dc.identifier.citation	Advances in Intelligent Systems and Computing, 2016, 392 pp. 29 - 32	en_US
dc.identifier.isbn	9783319245584	en_US
dc.identifier.issn	2194-5357	en_US
dc.identifier.uri	http://hdl.handle.net/10453/117408
dc.description.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%.	en_US
dc.relation.ispartof	Advances in Intelligent Systems and Computing	en_US
dc.relation.isbasedon	10.1007/978-3-319-24560-7_4	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Table tennis and computer vision: A monocular event classifier	en_US
dc.type	Conference Proceeding
utslib.citation.volume	392	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US
pubs.volume	392	en_US

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%.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/117408