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)

217 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
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 - Sept 2003
Event	International Conference on Image Processing, ICIP 2003 - Barcelona, Spain Duration: 14 Sept 2003 → 17 Sept 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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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

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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]; International Conference on Image Processing, ICIP 2003 ; 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",

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AU - Petkovic, M.

AU - Mihajlovic, V.

AU - Jonker, Willem

N1 - Imported from EWI/DB PMS [db-utwente:inpr:0000003595]

PY - 2003/9

Y1 - 2003/9

N2 - 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.

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.

KW - DB-MMR: MULTIMEDIA RETRIEVAL

KW - METIS-216365

KW - IR-46872

KW - EWI-7308

U2 - 10.1109/ICIP.2003.1246754

DO - 10.1109/ICIP.2003.1246754

M3 - Conference contribution

SN - 0-7803-7750-8

T3 - IEEE Conference Proceedings

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EP - 614

BT - Proceedings of the International Conference on Image Processing (ICIP 2003)

PB - IEEE

CY - Los Alamitos

T2 - International Conference on Image Processing, ICIP 2003

Y2 - 14 September 2003 through 17 September 2003

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