A Three-Level Scheme for Real-Time Ball Tracking

Tong, Xiaofeng; Wang, Tao; Li, Wenlong; Zhang, Yimin; Yang, Bo; Wang, Fei; Sun, Lijuan

doi:10.1007/978-3-540-73417-8_23

Cited by 6 publications

(1 citation statement)

References 3 publications

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“…An general overview of sports informatics technologies can be found in [1]. Our work relates to various works that detect and track certain objects in sports, e.g., tennis [25], table tennis [6], soccer [10,17,24], basketball [3], volleyball [4,5], or even pool billiard [18,19]. The best known multi camera technology that works with player and ball tracking is probably the "Hawk Eye" [13], widely used in tennis sports, not only to aid referees but also to collect statistical data (mostly for audiences and TV).…”

Section: Related Workmentioning

confidence: 99%

Using Video Analysis and Machine Learning for Predicting Shot Success in Table Tennis

Draschkowitz¹,

Draschkowitz²,

Hlavacs³

2015

EAI Endorsed Transactions on Creative Technologies

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Coaching professional ball players has become more and more difficult 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.

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