Imperial College London
Alternate

ProfessorKrystianMikolajczyk

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor in Computer Vision and Machine Learning
 
 
 
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Contact

 

+44 (0)20 7594 6220k.mikolajczyk

 
 
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Location

 

Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Yan:2014:10.1016/j.imavis.2014.08.004,
author = {Yan, F and Kittler, J and Windridge, D and Christmas, W and Mikolajczyk, K and Cox, S and Huang, Q},
doi = {10.1016/j.imavis.2014.08.004},
journal = {Image and Vision Computing},
pages = {896--903},
title = {Automatic annotation of tennis games: An integration of audio, vision, and learning},
url = {http://dx.doi.org/10.1016/j.imavis.2014.08.004},
volume = {32},
year = {2014}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Yan,F
AU  - Kittler,J
AU  - Windridge,D
AU  - Christmas,W
AU  - Mikolajczyk,K
AU  - Cox,S
AU  - Huang,Q
DO  - 10.1016/j.imavis.2014.08.004
EP  - 903
PY  - 2014///
SN  - 0262-8856
SP  - 896
TI  - Automatic annotation of tennis games: An integration of audio, vision, and learning
T2  - Image and Vision Computing
UR  - http://dx.doi.org/10.1016/j.imavis.2014.08.004
VL  - 32
ER  -
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