Techniques for Automatic Video Content Derivation

M. Petkovic; V. Mihajlovic; Willem Jonker

doi:10.1109/ICIP.2003.1246754

Techniques for Automatic Video Content Derivation

M. Petkovic, V. Mihajlovic, Willem Jonker

Databases (Former)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

6 Citations (Scopus)

90 Downloads (Pure)

Abstract

In this paper, we focus on the use of three different techniques that support automatic derivation of video content from raw video data, namely, a spatio-temporal rule-based method, hidden Markov models, and dynamic Bayesian networks. These techniques are validated in the particular domain of tennis and Formula 1 race videos. We present the experimental results for the detection of events such as net-playing, rally, service, and forehand stroke among others in the Tennis domain, as well as excited speech, start, fly-out, passing, and highlights in the Formula 1 domain.

Original language	Undefined
Title of host publication	Proceedings of the International Conference on Image Processing (ICIP 2003)
Place of Publication	Los Alamitos
Publisher	IEEE Computer Society
Pages	611-614
Number of pages	4
ISBN (Print)	0-7803-7750-8
DOIs	https://doi.org/10.1109/ICIP.2003.1246754
Publication status	Published - Sep 2003
Event	International Conference on Image Processing, ICIP 2003 - Barcelona, Spain Duration: 14 Sep 2003 → 17 Sep 2003

Publication series

Name	IEEE Conference Proceedings
Publisher	IEEE Computer Society
Volume	2
ISSN (Print)	1522-4880

Conference

Conference	International Conference on Image Processing, ICIP 2003
Period	14/09/03 → 17/09/03
Other	14-17 Sept. 2003

Keywords

DB-MMR: MULTIMEDIA RETRIEVAL
METIS-216365
IR-46872
EWI-7308

Access to Document

10.1109/ICIP.2003.1246754

01246754

http://eprints.eemcs.utwente.nl/secure2/7308/01/01246754.pdf

Cite this

@inproceedings{ae5dfe1b63204b04a7ba100f47af093f,

title = "Techniques for Automatic Video Content Derivation",

abstract = "In this paper, we focus on the use of three different techniques that support automatic derivation of video content from raw video data, namely, a spatio-temporal rule-based method, hidden Markov models, and dynamic Bayesian networks. These techniques are validated in the particular domain of tennis and Formula 1 race videos. We present the experimental results for the detection of events such as net-playing, rally, service, and forehand stroke among others in the Tennis domain, as well as excited speech, start, fly-out, passing, and highlights in the Formula 1 domain.",

keywords = "DB-MMR: MULTIMEDIA RETRIEVAL, METIS-216365, IR-46872, EWI-7308",

author = "M. Petkovic and V. Mihajlovic and Willem Jonker",

note = "Imported from EWI/DB PMS [db-utwente:inpr:0000003595]; null ; Conference date: 14-09-2003 Through 17-09-2003",

year = "2003",

month = sep,

doi = "10.1109/ICIP.2003.1246754",

language = "Undefined",

isbn = "0-7803-7750-8",

series = "IEEE Conference Proceedings",

publisher = "IEEE Computer Society",

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booktitle = "Proceedings of the International Conference on Image Processing (ICIP 2003)",

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Petkovic, M, Mihajlovic, V & Jonker, W 2003, Techniques for Automatic Video Content Derivation. in Proceedings of the International Conference on Image Processing (ICIP 2003). IEEE Conference Proceedings, vol. 2, IEEE Computer Society, Los Alamitos, pp. 611-614, International Conference on Image Processing, ICIP 2003, 14/09/03. https://doi.org/10.1109/ICIP.2003.1246754

Techniques for Automatic Video Content Derivation. / Petkovic, M.; Mihajlovic, V.; Jonker, Willem.

Proceedings of the International Conference on Image Processing (ICIP 2003). Los Alamitos : IEEE Computer Society, 2003. p. 611-614 (IEEE Conference Proceedings; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

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AU - Mihajlovic, V.

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AB - In this paper, we focus on the use of three different techniques that support automatic derivation of video content from raw video data, namely, a spatio-temporal rule-based method, hidden Markov models, and dynamic Bayesian networks. These techniques are validated in the particular domain of tennis and Formula 1 race videos. We present the experimental results for the detection of events such as net-playing, rally, service, and forehand stroke among others in the Tennis domain, as well as excited speech, start, fly-out, passing, and highlights in the Formula 1 domain.

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