Automatic annotation of tennis games: an integration of audio, vision, and learning

Article


Yan, F., Kittler, J., Windridge, D., Christmas, W., Mikolajczyk, K., Cox, S. and Huang, Q. 2014. Automatic annotation of tennis games: an integration of audio, vision, and learning. Image and Vision Computing. 32 (11), pp. 896-903. https://doi.org/10.1016/j.imavis.2014.08.004
TypeArticle
TitleAutomatic annotation of tennis games: an integration of audio, vision, and learning
AuthorsYan, F., Kittler, J., Windridge, D., Christmas, W., Mikolajczyk, K., Cox, S. and Huang, Q.
Abstract

Fully automatic annotation of tennis game using broadcast video is a task with a great potential but with enormous challenges. In this paper we describe our approach to this task, which integrates computer vision, machine listening, and machine learning. At the low level processing, we improve upon our previously proposed state-of-the-art tennis ball tracking algorithm and employ audio signal processing techniques to detect key events and construct features for classifying the events. At high level analysis, we model event classification as a sequence labelling problem, and investigate four machine learning techniques using simulated event sequences. Finally, we evaluate our proposed approach on three real world tennis games, and discuss the interplay between audio, vision and learning. To the best of our knowledge, our system is the only one that can annotate tennis game at such a detailed level.

LanguageEnglish
PublisherElsevier
JournalImage and Vision Computing
ISSN0262-8856
Publication dates
Print06 Nov 2014
Publication process dates
Deposited22 Apr 2016
Accepted04 Aug 2014
Output statusPublished
Accepted author manuscript
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Additional information

Available online 11 August 2014

Digital Object Identifier (DOI)https://doi.org/10.1016/j.imavis.2014.08.004
Scopus EID2-s2.0-84906837358
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https://repository.mdx.ac.uk/item/864v1

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