Table tennis and computer vision: a monocular event classifier

© 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...

Full description

Saved in:
Bibliographic Details
Main Authors: Kevin M. Oldham, Paul Chung, Eran Edirisinghe, Ben Halkon
Format: Default Conference proceeding
Published: 2016
Subjects:
Other information and computing sciences not elsewhere classified
Event classification
Table tennis
Ball
Segmentation
Detection
Computer vision
Optical flow
Information and Computing Sciences not elsewhere classified
Online Access:https://hdl.handle.net/2134/20220
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:© 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%.

Similar Items