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

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.

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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. © 2014 Elsevier B.V.

Item Type: Article
Authors :
NameEmailORCID
Yan, FUNSPECIFIEDUNSPECIFIED
Kittler, JUNSPECIFIEDUNSPECIFIED
Windridge, DUNSPECIFIEDUNSPECIFIED
Christmas, WUNSPECIFIEDUNSPECIFIED
Mikolajczyk, KUNSPECIFIEDUNSPECIFIED
Cox, SUNSPECIFIEDUNSPECIFIED
Huang, QUNSPECIFIEDUNSPECIFIED
Date : November 2014
Identification Number : 10.1016/j.imavis.2014.08.004
Depositing User : Symplectic Elements
Date Deposited : 28 Mar 2017 13:12
Last Modified : 31 Oct 2017 16:58
URI: http://epubs.surrey.ac.uk/id/eprint/806141

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