Abstract
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%.
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Oldham, K.M., Chung, P.W.H., Edirisinghe, E.A., Halkon, B.J. (2016). Table tennis and computer vision: a monocular event classifier. In: Chung, P., Soltoggio, A., Dawson, C., Meng, Q., Pain, M. (eds) Proceedings of the 10th International Symposium on Computer Science in Sports (ISCSS). Advances in Intelligent Systems and Computing, vol 392. Springer, Cham. https://doi.org/10.1007/978-3-319-24560-7_4
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DOI: https://doi.org/10.1007/978-3-319-24560-7_4
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