Research Article
Using Video Analysis and Machine Learning for Predicting Shot Success in Table Tennis
@ARTICLE{10.4108/eai.20-10-2015.150096, author={Lukas Draschkowitz and Christoph Draschkowitz and Helmut Hlavacs}, title={Using Video Analysis and Machine Learning for Predicting Shot Success in Table Tennis}, journal={EAI Endorsed Transactions on Creative Technologies}, volume={2}, number={5}, publisher={EAI}, journal_a={CT}, year={2015}, month={10}, keywords={machine learning, sports video analysis, ball tracking, video processing, video information retrieval, video mining, multimedia data mining}, doi={10.4108/eai.20-10-2015.150096} }- Lukas Draschkowitz
Christoph Draschkowitz
Helmut Hlavacs
Year: 2015
Using Video Analysis and Machine Learning for Predicting Shot Success in Table Tennis
CT
EAI
DOI: 10.4108/eai.20-10-2015.150096
Abstract
Coaching professional ball players has become more and more dicult and requires among other abilities also good tactical knowledge. This paper describes a program that can assist in tactical coaching for table tennis by extracting and analyzing video data of a table tennis game. The here described application automatically extracts essential information from a table tennis match, such as speed, length, height and others, by analyzing a video of that game. It then uses the well known machine learning library \Weka" to learn about the success of a shot. Generalization is tested by using a training and a test set. The program then is able to predict the outcome of shots with high accuracy. This makes it possible to develop and verify tactical suggestions for players as part of an automatic analyzing and coaching tool, completely independent of human interaction.
Copyright © 2015 L. Draschkowitz et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.