Techniques for automatic video content derivation

Author

Petkovic, Milan ; Mihajlovic, Vojkan ; Jonker, Willem

Author_Institution

Twente Univ., Enschede, Netherlands

Volume

2

fYear

2003

fDate

14-17 Sept. 2003

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

belief networks; content-based retrieval; hidden Markov models; image retrieval; spatiotemporal phenomena; video signal processing; Formula 1 race video; automatic video content derivation technique; content-based video retrieval; dynamic Bayesian network; event detection; forehand stroke event; hidden Markov model; net-playing event; rally event; raw video data; spatio-temporal rule-based method; tennis domain; video processing; Bayesian methods; Computer aided software engineering; Content based retrieval; Data mining; Event detection; Hidden Markov models; Information retrieval; Shape; Speech; TV broadcasting;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 2003. ICIP 2003. Proceedings. 2003 International Conference on

ISSN

1522-4880

Print_ISBN

0-7803-7750-8

Type

conf

DOI

10.1109/ICIP.2003.1246754

Filename

1246754