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

Yan, Fei, Kittler, Josef, Windridge, David ORCID: https://orcid.org/0000-0001-5507-8516, Christmas, William, Mikolajczyk, Krystian, Cox, Stephen and Huang, Qiang (2014) Automatic annotation of tennis games: an integration of audio, vision, and learning. Image and Vision Computing, 32 (11). pp. 896-903. ISSN 0262-8856 (doi:10.1016/j.imavis.2014.08.004)

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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.

Item Type: Article
Additional Information: Available online 11 August 2014
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 19482
Useful Links:
Depositing User: David Windridge
Date Deposited: 22 Apr 2016 10:31
Last Modified: 03 Jun 2019 11:21
URI: https://eprints.mdx.ac.uk/id/eprint/19482

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