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

Yan, Fei, Kittler, Josef, Windridge, David, 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

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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
Uncontrolled Keywords: tennis annotation,object tracking,audio event classification,sequence labelling,structured output learning,hidden markov model
Faculty \ School: Faculty of Science > School of Computing Sciences
Depositing User: Pure Connector
Date Deposited: 08 Oct 2014 08:48
Last Modified: 21 Apr 2020 23:29
URI: https://ueaeprints.uea.ac.uk/id/eprint/50428
DOI: 10.1016/j.imavis.2014.08.004

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