Techniques for Automatic Video Content Derivation

M. Petkovic, V. Mihajlovic, Willem Jonker

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

5 Citations (Scopus)
58 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 languageUndefined
Title of host publicationProceedings of the International Conference on Image Processing (ICIP 2003)
Place of PublicationLos Alamitos
PublisherIEEE Computer Society
Pages611-614
Number of pages4
ISBN (Print)0-7803-7750-8
DOIs
Publication statusPublished - Sep 2003

Publication series

NameIEEE Conference Proceedings
PublisherIEEE Computer Society
Volume2
ISSN (Print)1522-4880

Keywords

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

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